Over the past 20 years, the increase in computing power by about eight to 10 orders of magnitude has brought the costs of using digital technologies down to a tiny fraction of their year 2000 levels (Figure 1).

Figure 1. The costs of using many technologies are dropping rapidly

Eight orders of magnitude is quite substantial: Lant Pritchett notes that it is the ratio of the United States’ GDP to his personal annual income. The decline in costs is unlike that for any other technological innovation. It would be the equivalent of automobiles costing between $2 and $21 today (the Ford Model-T cost $850 in 1908 or $21,340 in 2012 dollars), or refrigerators costing between $1.50 and $15 today (they cost around $14,000 when they first came out).

During this period, levels of overall government effectiveness have hardly changed (Figure 2).

Figure 2. Government effectiveness scores

Source: Worldwide Governance Indicators, 2017

To be sure, there have been some successes in digital technology improving certain government programs and processes. Biometric registration, verification, and payment systems in India’s National Rural Employment Guarantee Scheme reduced leakages of funds by 35 percent. Electronic tendering of contracts increased procurement competitiveness and the quality of roads in India and Indonesia. One-stop, computerized service centers in Karnataka, India enabled citizens to receive birth and death certificates with, on average, 3.4 fewer visits, 58 fewer minutes per visit, and 50 percent less chance of being asked for a bribe compared to a typical government office. And mobile phone based monitoring of service providers reduced their absence rates by as much as 25 percentage points in India, Pakistan, and Uganda.

Despite these individual successes and considerable evidence that technology has improved productivity in the private sector, overall public-sector effectiveness does not seem to have been affected. Why not?

Three possibilities:

1. As the World Development Report 2016 suggested, technology requires complementary analog inputs to register an increase in effectiveness. Even if routine tasks can be automated, the final output requires human intuition, judgment, and discretion to be productive. While this argument is compelling in the abstract, it is hard to imagine that an eight-orders-of-magnitude decline in the price of the digital input hasn’t had some effect on this joint product. The elasticity of substitution between the digital and analog inputs must be precisely zero for this to be the reason that overall government effectiveness has not improved.

2. Public bureaucracies either do not want to—or cannot—adapt to digital technologies. They may not want to because, in a government setting, efficiency improvements may lead to a reduction in the agency’s budget. Also, better record keeping and greater transparency make it harder for bureaucrats to capture the rents from government procedures.

3. Digital technology creates monopolies, whereas the function of government is either to break up monopolies or to prevent becoming a monopoly of its own. Technology creates monopolies because the marginal cost of production goes down, and the marginal benefit to the user goes up, with the number of users in the system, as Uber or Facebook have shown. The private sector has embraced technology and seen increases in productivity precisely because it has enabled them to exercise monopoly power. The essential reason is that, with computers, the identity of the machine can be known easily (the IP address), but the identity of the user can only be known if the user provides it. This creates a huge incentive to centralize information about users, which gives the owner of that information monopoly power.

This centralization of information cuts both ways for government effectiveness. On the one hand, for those activities where discretion at the local level is not needed, centralized information can exploit economies of scale and improve efficiency. Among the success stories mentioned above, payment systems, procurement, and issuing birth certificates are all examples of activities that require very little discretion at the local level. On the other hand, for a large number of public-sector activities, including teaching, there has to be a balance between centralized and decentralized authority. While the central authority can monitor whether the teacher is absent or not, it is only information at the classroom level that enables the teacher to teach effectively. In fact, even the benefits of reduced teacher absenteeism seem to have dissipated after a few years.

In short, to improve government effectiveness across the board, the technology needs to be adapted to situations where some information is held at the local level while other information is at the central level. Interestingly, Tim Berners-Lee, the founder of the World Wide Web, is developing a similar system whereby all of a person’s information is stored in a Personal Online Data (POD) store that only the individual can access, while the internet remains free. Such a system may give governments the appropriate flexibility to tailor their systems and enhance overall effectiveness.

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https://www.brookings.edu/wp-content/uploads/2019/01/global_india_computer_001.jpg?w=282By Shanta Devarajan
Over the past 20 years, the increase in computing power by about eight to 10 orders of magnitude has brought the costs of using digital technologies down to a tiny fraction of their year 2000 levels (Figure 1).
Figure 1. The costs of using many technologies are dropping rapidly
From Charting Pathways for Inclusive Growth: From Paralysis to Preparation, Pathways to Prosperity Commission, 2018.
Eight orders of magnitude is quite substantial: Lant Pritchett notes that it is the ratio of the United States’ GDP to his personal annual income. The decline in costs is unlike that for any other technological innovation. It would be the equivalent of automobiles costing between $2 and $21 today (the Ford Model-T cost $850 in 1908 or $21,340 in 2012 dollars), or refrigerators costing between $1.50 and $15 today (they cost around $14,000 when they first came out).
During this period, levels of overall government effectiveness have hardly changed (Figure 2).
Figure 2. Government effectiveness scores
Source: Worldwide Governance Indicators, 2017
To be sure, there have been some successes in digital technology improving certain government programs and processes. Biometric registration, verification, and payment systems in India’s National Rural Employment Guarantee Scheme reduced leakages of funds by 35 percent. Electronic tendering of contracts increased procurement competitiveness and the quality of roads in India and Indonesia. One-stop, computerized service centers in Karnataka, India enabled citizens to receive birth and death certificates with, on average, 3.4 fewer visits, 58 fewer minutes per visit, and 50 percent less chance of being asked for a bribe compared to a typical government office. And mobile phone based monitoring of service providers reduced their absence rates by as much as 25 percentage points in India, Pakistan, and Uganda.
Despite these individual successes and considerable evidence that technology has improved productivity in the private sector, overall public-sector effectiveness does not seem to have been affected. Why not?
Three possibilities:
1. As the World Development Report 2016 suggested, technology requires complementary analog inputs to register an increase in effectiveness. Even if routine tasks can be automated, the final output requires human intuition, judgment, and discretion to be productive. While this argument is compelling in the abstract, it is hard to imagine that an eight-orders-of-magnitude decline in the price of the digital input hasn’t had some effect on this joint product. The elasticity of substitution between the digital and analog inputs must be precisely zero for this to be the reason that overall government effectiveness has not improved.
2. Public bureaucracies either do not want to—or cannot—adapt to digital technologies. They may not want to because, in a government setting, efficiency improvements may lead to a reduction in the agency’s budget. Also, better record keeping and greater transparency make it harder for bureaucrats to capture the rents from government procedures.
Bureaucracies may not be able to adapt to new technologies because they simply lack the infrastructure. In Ethiopia and Nigeria, local government officials have access to the internet 22 percent and 3 percent of days, respectively. Furthermore, public officials continue to use tacit knowledge to inform decisions. In Ethiopia, 90 percent of public officials get information from field visits and personal interactions, rather than management information systems.
But lack of investment in technology cannot be the whole story, since several countries such as India and Rwanda have a digital adoption index that is higher than their per capita GDP would predict. Yet neither country has seen an improvement in its government effectiveness index.
3. Digital technology creates monopolies, whereas the function of government ... By Shanta Devarajan
Over the past 20 years, the increase in computing power by about eight to 10 orders of magnitude has brought the costs of using digital technologies down to a tiny fraction of their year 2000 levels (Figure 1)https://www.brookings.edu/blog/techtank/2019/01/11/time-to-move-beyond-5g-hype/Time to move beyond 5G hypehttp://webfeeds.brookings.edu/~/591652544/0/brookingsrss/topics/technology~Time-to-move-beyond-G-hype/
Fri, 11 Jan 2019 15:24:18 +0000https://www.brookings.edu/?p=557109

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By Tom Wheeler

The next generation wireless network known as 5G is like an abstract painting—you can make of it what you want.

To some, 5G is “wireless cable,” the means to deliver video and high-speed internet much like a cable system.

To others, 5G is the transformational technology necessary to handle the flood of data to enable new capabilities such as autonomous vehicles and remote surgery.

To Sprint and T-Mobile, the cost of building out 5G is the justification for a merger, reducing consumer choice from four national wireless carriers to three.

To AT&T, a faster 4G network has been rebranded “5G-E” although it is not “real 5G.”

To satellite companies currently licensed to use potential 5G spectrum, it is the opportunity to sell something for which they paid nothing at a windfall profit.

To Donald Trump, 5G is a political talking point to gin up nationalistic furor over a “race” with China.

To the Trump Federal Communications Commission (FCC), 5G is an excuse to preempt the antenna siting decisions of local governments.

To the wireless industry, 5G is the chance to promote something new that may open up needed new revenue streams.

It is time to bring some order to the chaos. It is time to move past the political and marketing talking points to consider both the promise of 5G and the challenge to its realization.

The wrong metaphor

First of all, to call 5G a “race” is a deceptive metaphor. A “race” connotes a contest along a common course with a start and finish. The reality is that 5G networks will be built piece-by-piece, area-by-area, and application-by-application over a protracted period of years. We must operate with a long-haul vision. There is nothing wrong with setting out to win the first lap, but arriving at the ultimate outcome will require long-term strategies and commitments.

If there is a “race,” the advantage will go to those with the broadest coverage. Without universal coverage, for instance, how can 5G be the network for controlling autonomous vehicles? Even today as the wireless companies’ herald the cities named for their 5G rollouts, they are not covering all neighborhoods in those cities. All the promise of 5G-controlled vehicles vanish if the vehicle cannot drive everywhere.

Of course, the last to be served is always rural America. According to the FCC, over 20 million rural Americans do not have access to a high-speed broadband network. Rural America was the last to see 4G wireless; unless something proactive occurs, the same fate will be true for 5G. All the great stories of how 5G will bring big city-quality remote surgery to rural clinics are an empty promise without the necessary coverage.

Time for a national strategy

The national strategy for 5G needs to move beyond slogans and press releases. We need a laser focus on the two factors most essential to the success of American 5G: ubiquitous coverage and network security.

If 5G is to live up to its promise, its rollout out must improve over 4G. Redlining the buildout areas of cities does not just deny service to those communities, but shrinking the service area also slows the development of 5G applications. Continuing the “rural last” precedent for broadband service will perpetuate the disenfranchisement of large numbers of American citizens.

Building 5G will be expensive. Building multiple redundant networks on top of each other only increases construction costs. Twenty-five years ago, at the dawn of digital wireless networks, the wireless industry saved money and expedited coverage in specific areas by building a single shared network. Consumers did not know they were sharing the same network, and competition between the companies remained fierce. But because costs were shared rather than duplicated, the new technology expanded more quickly. If the Trump Administration encouraged the wireless companies to construct a shared network, it would be built faster and cover more people—including in rural areas—while still offering consumers competitive choice.

Unfortunately, the already-substantial cost of building 5G is rising. While President Trump has said, “It is imperative that America be first in fifth-generation (5G) wireless technologies,” his trade war with China makes it more expensive for American companies than for Chinese companies. FCC Commissioner Jessica Rosenworcel has pointed out the trade war, “threatens to increase the costs of wireless infrastructure by hundreds of millions of dollars at a critical moment” since much of the gear will be assessed a special tariff of up to 25 percent.

Protecting the network

The 5G network must also be cyber-secure. The cyber threat to 5G prompted the Trump National Security Council staff to search for solutions to protect the networks, including national ownership of a shared network. After the report was leaked and the industry complained, the official responsible for the report—an Air Force general who had been defense attaché in Beijing—was fired. President Trump’s latest presidential memorandum on 5G never mentions the word cyber.

Simply put, a cyber-insecure network will not be used as much or inspire innovators to build new applications. Unfortunately, the Trump FCC eliminated the 5G cyber protection plan begun by the Obama FCC. The Trump FCC has even questioned whether the agency entrusted with the nation’s networks has any responsibility for the cybersecurity of those networks.

The United States needs to go all-in on 5G. That means doing better than we did on 4G to speed ubiquitous service to all Americans. That also means demanding a cyber-secure network. Unfortunately, in lieu of leadership for such a strategy, we are being fed slogans and told to go to races.

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By Tom Wheeler
The next generation wireless network known as 5G is like an abstract painting—you can make of it what you want.
- To some, 5G is “wireless cable,” the means to deliver video and high-speed internet much like a cable system. - To others, 5G is the transformational technology necessary to handle the flood of data to enable new capabilities such as autonomous vehicles and remote surgery. - To Sprint and T-Mobile, the cost of building out 5G is the justification for a merger, reducing consumer choice from four national wireless carriers to three. - To AT&T, a faster 4G network has been rebranded “5G-E” although it is not “real 5G.” - To satellite companies currently licensed to use potential 5G spectrum, it is the opportunity to sell something for which they paid nothing at a windfall profit. - To Donald Trump, 5G is a political talking point to gin up nationalistic furor over a “race” with China. - To the Trump Federal Communications Commission (FCC), 5G is an excuse to preempt the antenna siting decisions of local governments. - To the wireless industry, 5G is the chance to promote something new that may open up needed new revenue streams.
It is time to bring some order to the chaos. It is time to move past the political and marketing talking points to consider both the promise of 5G and the challenge to its realization.
The wrong metaphor
First of all, to call 5G a “race” is a deceptive metaphor. A “race” connotes a contest along a common course with a start and finish. The reality is that 5G networks will be built piece-by-piece, area-by-area, and application-by-application over a protracted period of years. We must operate with a long-haul vision. There is nothing wrong with setting out to win the first lap, but arriving at the ultimate outcome will require long-term strategies and commitments.
If there is a “race,” the advantage will go to those with the broadest coverage. Without universal coverage, for instance, how can 5G be the network for controlling autonomous vehicles? Even today as the wireless companies’ herald the cities named for their 5G rollouts, they are not covering all neighborhoods in those cities. All the promise of 5G-controlled vehicles vanish if the vehicle cannot drive everywhere.
Of course, the last to be served is always rural America. According to the FCC, over 20 million rural Americans do not have access to a high-speed broadband network. Rural America was the last to see 4G wireless; unless something proactive occurs, the same fate will be true for 5G. All the great stories of how 5G will bring big city-quality remote surgery to rural clinics are an empty promise without the necessary coverage.
Time for a national strategy
The national strategy for 5G needs to move beyond slogans and press releases. We need a laser focus on the two factors most essential to the success of American 5G: ubiquitous coverage and network security.
If 5G is to live up to its promise, its rollout out must improve over 4G. Redlining the buildout areas of cities does not just deny service to those communities, but shrinking the service area also slows the development of 5G applications. Continuing the “rural last” precedent for broadband service will perpetuate the disenfranchisement of large numbers of American citizens.
Building 5G will be expensive. Building multiple redundant networks on top of each other only increases construction costs. Twenty-five years ago, at the dawn of digital wireless networks, the wireless industry saved money and expedited coverage in specific areas by building a single shared network. Consumers did not know they were sharing the same network, and competition between the companies remained fierce. But because costs were shared rather than duplicated, the new technology expanded more quickly. If the Trump Administration encouraged the ... By Tom Wheeler
The next generation wireless network known as 5G is like an abstract painting—you can make of it what you want.
- To some, 5G is “wireless cable,” the means to deliver video and high-speed internet much like a ... https://www.brookings.edu/articles/china-is-taking-over-indias-tech-space-should-we-worry/China is taking over India’s tech space. Should we worry?http://webfeeds.brookings.edu/~/591468454/0/brookingsrss/topics/technology~China-is-taking-over-India%e2%80%99s-tech-space-Should-we-worry/
Thu, 10 Jan 2019 11:42:18 +0000https://www.brookings.edu/?post_type=article&p=556812

Under President Donald Trump, great power competition has become the organizing principle of American foreign policy. This has led to near-daily invocations of the Cold War to describe the intensifying rivalry between the United States and China, and to frequent analogies to an “arms race” to describe bilateral competition in advanced technologies, including quantum computing and artificial intelligence (AI). Public statements and national plans from both governments have reinforced this zero-sum dynamic. Such framing has done more to conceal than clarify and, if taken to its logical end-point, will do more harm than good for the United States.

AI will create both immense stress on the U.S.-China relationship as well as opportunities for potential collaboration.

This paper argues that we need a different narrative to describe the role of AI in the escalating competition between the United States and China. Even as artificial intelligence is contributing to an intensifying bilateral rivalry, it also is driving both countries to race out ahead of the rest of the world in innovation, economic growth, and overall national power. Moreover, the adoption of advanced technologies is hastening the arrival of intense societal disruptions in both countries. AI applications are also exacerbating ethical questions about government’s role in protecting individual liberties, and elevating the competition between authoritarian capitalism and liberal democracy. To focus on only one of these dynamics would be to lose sight of the bigger picture: AI will create both immense stress on the U.S.-China relationship as well as opportunities for potential collaboration. The core challenge for U.S. policymakers will be to manage the stresses induced by AI in a way that preserves political space for working together when it serves American interests to do so. Along with other essays in our AI policy series, this piece offers recommendations on how best to do so.

How did we get here?

In March 2016, a Google system powered by an AI algorithm squared off against Lee Sedol, an 18-time world champion in the famously complex game of Go. In front of an audience of more than 280 million mostly Chinese viewers, the Google system triumphed, plunging China into what renowned technologist Kai-Fu Lee described as an “artificial intelligence fever” that “lit a fire under the Chinese technology community that has been burning ever since.”1

A little over one year later, in July 2017, China unveiled its national plan for seizing the spoils of AI. The “New Generation AI Development Plan” set targets and pledged national resources, calling for China to catch up on AI technology and applications by 2020, achieve major breakthroughs by 2025, and become a global leader in AI by 2030. President Xi Jinping reinforced these themes in his 19th Party Congress speech in October 2017 and in a major Politburo study session in late October.

Further stoking unease has been some of China’s official rhetoric, which promotes military-civil fusion of technological development to degrade America’s competitive edge. Such unease has been amplified by China’s ambitious Belt and Road Initiative, a massive global initiative that some in the United States fear will enable Beijing to set global technological standards. Cumulatively, China’s efforts have fed what Dean Garfield, president and CEO of the Information Technology Industry Council, has characterized as a newfound “hysteria” in Washington that America is losing its innovation edge to China.2

Americans are unaccustomed to other countries publicly projecting plans to displace them. As former Secretary of State Condoleezza Rice recently said, “When we see [China]…say, ‘We’re going to do whatever it takes to surpass the United States’… you’re going to get a response from the United States.”3 Thus far, a large part of that response has been attempting to slow China’s progress, including by tightening screening of foreign investments in core technologies, scrutinizing Chinese academic exchanges, applying targeted tariffs to reduce China’s competitiveness in key sectors, increasing prosecutions of Chinese actors involved in economic espionage, and investing greater resources in counter-intelligence operations.

To be clear, it is fair and appropriate for countries to defend their economic crown jewels from foreign exploitation or infringement. Like any other country, the United States has the right to defend itself, and should continue to do so vigorously. But in protecting itself, the United States needs to avoid inflicting self-harm. Arguments for “decoupling” the economic relationship between the United States and China—including by collapsing ICT supply chains—would do just that. America’s leading sources of innovation increasingly are found in its technology sector, which is deeply intertwined with China’s. There are high levels of collaboration between researchers and engineers in both countries, manifesting in growing numbers of jointly authored peer-reviewed academic papers and deep levels of joint investments by U.S. and Chinese venture capital firms into AI-related enterprises in both countries.

As Lorand Laskai and Samm Sacks recently argued, fencing off the U.S. technology sector from China would cede ground to Chinese competitors, slow down new breakthroughs, reduce the competitiveness of American firms, and increase costs for American consumers.4 Determining what key U.S. technologies to protect for national security purposes will require policy precision in order to avoid a brickbat approach that undermines American innovation. Former Secretary of Defense Robert Gates once dubbed this the “small yard, high fence” strategy – selectively protecting key technologies, and doing so aggressively.

Focusing on the big picture: U.S. and China separating from the pack

In the process of protecting itself, the United States must endeavor not to lose sight of the bigger picture. While competition between the United States and China is intensifying, these two powers are increasing the distance between themselves and every other country in the world in terms of economic size, pace of innovation, and overall national power. This separation of the United States and China from the rest of the pack is being fueled largely by both countries’ technology sectors. According to a widely cited study by PricewaterhouseCoopers, the United States and China are set to capture 70 percent of the $15.7 trillion windfall that AI is expected to add to the global economy by 2030.

Both countries are being propelled forward in AI by unique attributes that no other country soon will replicate. These include world-class research expertise, deep capital pools, data abundance, largely supportive policy environments, and highly competitive innovation ecosystems. Of the roughly 4,500 AI-involved companies in the world, about half operate in the United States and one-third operate in China. So, while it is fair to say the United States and China are competing against each other, the larger truth is that both countries also are navigating the frontier of innovation simultaneously.

This is where a purely competitive zero-sum framing does a disservice to both. When every step forward by one is viewed as a setback for the other, there is disincentive to coordinate on shared challenges or be open to learning from the other’s experiences.

Identifying areas of competition and cooperation

One way of overcoming the trend toward all-encompassing competition would be for the United States and China to develop a better shared understanding of where cooperation would be mutually beneficial and where inherent conflicts of interests will need to be managed. This would enable both sides to build cooperation where interests align, which in turn would give both sides greater confidence to deal with issues where they diverge. Below is an illustrative – not exhaustive – set of examples, broken down into four categories: military and security; trade; politics; and society.

Military and security

The military domain presents the greatest risk for miscalculation. It also is where the need is greatest for ongoing, direct, authoritative bilateral communication to develop a better shared understanding of ethical boundaries around AI, particularly given the potential implications for warfighting.

The military domain presents the greatest risk for miscalculation. It also is where the need is greatest for ongoing, direct, authoritative bilateral communication to develop a better shared understanding of ethical boundaries around AI.

The bilateral relationship already faces an acute security dilemma, where actions on one side make the other feel less secure and push it to develop countermeasures. As AI technologies become more integrated into weapons systems and those systems gain autonomous capabilities, this security dilemma could grow more pronounced, causing each side to nationalize innovation streams and limit transparency in order to seek an edge over the other. In other words, an existing security dilemma could quickly morph into an AI nightmare.

The stakes are high. As others have pointed out, the United States and China stand on the cusp of rapid change in the conduct of war, not unlike the employment of cavalry, the advent of the rifled musket, or the merging of fast armor with air support to achieve a blitzkreig.5 Both countries are investing heavily to merge AI-enhanced capabilities and enable machine-based decision processes with minimal human interaction.

In the event of a confrontation between U.S. and Chinese forces (e.g., in the South China Sea), robotics and AI could play a critical role. Rapid escalation is an acute risk, particularly if the pace of technological advancements in capabilities exceeds the development of protocols for maintaining human agency in decision-making loops.

The real possibility of unintended and rapid escalation should provide incentive for both sides to begin developing boundaries around uses of AI in warfighting. The normative development process around previous arms control treaties, including the Chemical Weapons Convention, could offer applicable lessons that the United States and China could draw from.

Trade

Intensifying technological competition between the United States and China also risks leading to separate technological spheres, with Europe, North America, South America, and Australia largely adopting American technology and standards, and Asia, Africa, and the Middle East adopting Chinese ones. The ongoing global competition between the United States and China over 5G standards may be an early indication of the battles to come over these boundaries.

Through the introduction of 5G networks, the United States and China will shape the development of next-generation mobile standards, spectrum allocation, and deployments in key markets and regions. Particularly as U.S.-China trade tensions persist, and as both Washington and Beijing seek to lock in overseas 5G markets, there is growing risk of a bifurcated, non-interoperable 5G ecosystem emerging. In such a scenario, one system likely would be led by the United States and supported by technology developed in Silicon Valley, and the other would be led by China and supported by its highly capable digital platform companies.6

When a less politically charged moment in the U.S.-China relationship arrives, leaders in both countries should examine – both individually and collectively – whether or not their interests are best served by hastening the bifurcation of the global technology sector into U.S. and Chinese spheres. In such a scenario, both sides would limit their expansion potential: China’s markets primarily would be in developing countries with limited resources for technology build-out, and U.S. companies would operate mostly in developed markets where competition would be fierce.

Politics

AI technologies have the potential to be highly disruptive of political relations between the United States and China. AI technologies could become a vehicle for intensifying ideological rivalry, particularly if one or both sides harness such technologies to interfere in the other’s domestic political affairs.

Russia’s interference into America’s 2016 presidential election raised awareness of U.S. vulnerabilities on this front. As Elaine Kamarck documents, Russia used misinformation and disinformation to suppress voter turnout in targeted geographic districts and among certain demographic groups. Russia also spread derogatory information to denigrate certain candidates, foremost Hillary Clinton. What if those actions were the election interference equivalent of Lindbergh’s test flight – proof of concept, but far from realization of potential?

Going forward, artificial intelligence technologies may enable even more intrusive interference into democratic elections, including by improving an adversary’s ability to target and persuade particular voting groups. In her piece “Malevolent Soft Power, AI, and the Threat to Democracy,” Kamarck envisions a future where polling or search algorithms are linked with artificial intelligence and a human voice to call swing voters and persuade them, in real-time, that a certain candidate will harm them on the issues they identify as important and that the alternative (i.e., preferred) candidate is committed to addressing their individual concerns. She described this as “high-frequency trading in political persuasion.”7 Alina Polyakova classifies tactics like this as examples of “AI-driven asymmetric warfare.” In her piece “Weapons of the Weak: Russia and AI-driven Asymmetric Warfare,” she warns of the perils that ever-improving, low-cost commercial tools present. “Whereas most Russian disinformation content has been static,” she writes, “advances in learning AI will turn disinformation dynamic” through the creation and dissemination of manipulated video and audio.8

Going forward, artificial intelligence technologies may enable even more intrusive interference into democratic elections, including by improving an adversary’s ability to target and persuade particular voting groups.

If external interference becomes more prevalent and the legitimacy of election results around the world increasingly are called into question, democracy’s appeal could dim and alternative models (e.g., China’s economically statist and politically Leninist system) could become more attractive. This could open the door for Beijing to argue in other capitals that its model delivers higher rates of economic growth and that democratic systems are brittle against manipulation and ineffective at equitably distributing benefits within society.

To be clear, no public evidence exists to indicate that China has meddled in U.S. domestic political affairs in a manner reflecting the hypothetical scenario described above. Even absent such meddling, though, there are still significant concerns in Washington about how Beijing is harnessing AI technologies to surveil its citizens and suppress domestic dissent. These concerns reside on two levels. The first is the manner in which Beijing is perfecting its ability to track its citizens’ movements, communications, spending habits, news consumption, and so on. The second concern is that China may seek to export these practices to foreign leaders who desire tighter control over their citizens. In effect, China’s AI-driven model of intrusive surveillance could challenge America’s long-running efforts to spread democratic principles around the world. Even if this occurs more by default than design, China’s exportation of its policies and technologies to other countries could intensify ideological competition between the United States and China.

In short, there is serious risk that AI-powered technologies could inflame political and ideological tensions between the United States and China. But this outcome is not a foregone conclusion. To mitigate the possibility, there needs to be serious, sober, and sustained bilateral engagement to identify boundaries around what constitutes state interference in election processes and political systems. In 2015, for example, President Obama and President Xi agreed that government-sponsored, cyber-enabled economic espionage for commercial gain is out of bounds, and both leaders subsequently attracted other bodies such as the G-20 and the Gulf Cooperation Council to embrace a similar understanding. Reports have surfaced in recent months, however, suggesting that China has resumed government-sponsored, cyber-enabled economic espionage for commercial gain. Given the ill will within the U.S. government following reports that China may not be abiding by the 2015 cyber agreement, discussions on the boundaries of acceptable government involvement in other countries’ political systems may need to begin at the Track II level and mature over time into official channels.

Society

As the world leaders in AI, the United States and China will be among the first to confront intense social disruptions from this new technology. AI could prove to be every bit as revolutionary as the introduction of electricity or the steam engine – innovations that hastened a shift from agrarian to industrial modes of production and accelerated urbanization.

Already, a cottage industry has formed to predict the range of job losses that could result from the adoption of AI and the widening use of robotics. While there is nothing yet approaching a consensus around the scale of dislocation, the low-end projections are sobering, and the high-end estimates are frightening. At the low end, a team of researchers at the Organisation for Economic Cooperation and Development found that 10 percent of jobs in the United States are at high risk of being automated.9 Former Treasury Secretary Larry Summers has predicted that the rise of AI could bring about unemployment for about a third of American men ages 25-54 by mid-century. Kai-Fu Lee predicts that within ten to twenty years, the United States technically will be capable of automating between 40 and 50 percent of jobs.10 A similar story also applies to China.

In the face of such disruptions, both the United States and China will face hard choices, such as how to reform their educational systems, cope with widening wealth inequality, determine whether some form of a universal basic income is needed to preserve social cohesion, reform social safety nets, develop new concepts around privacy, and find productive ways for displaced workers to feel connected to society.

Each country also will contend with how to seize opportunities presented by AI to improve the national condition. A prime example is health care. The U.S. and China are the world’s two largest health care markets, and both countries are projected to experience a surge in spending over the coming decades as their populations age. Both would benefit from jointly leveraging AI applications for image analysis and diagnosis, discovering cures for cancer and other diseases, and identifying the most efficient care models for treatable conditions. Beyond health care, both countries could benefit from sharing data and expertise on major challenges like weather modeling, efficient energy use, tracking the effects of climate change, increasing access to education, enhancing wildlife conservation, and identifying and responding to illegal, unreported, and unregulated fishing. Both countries also could work together on standard-setting for new technologies, which could create greater efficiencies for bringing new products, such as driverless cars, to market.

Steps to improve America’s ability to work with China on AI developments

Given the magnitude of risks and opportunities on the horizon, and the fact that the United States and China simultaneously will be navigating uncharted territory in dealing with the societal dislocations caused by AI, it is imperative that both countries have candid discussions about how to effectively manage AI developments. These discussions should be guided by the objectives of managing risks and seizing opportunities. Many of these conversations likely will begin outside of government channels, a circumstance that reflects the private sector’s key role in AI innovation. Baidu’s recent decision to join the U.S.-based Partnership on AI provides an encouraging example of how leading U.S. and Chinese actors can come together to establish best practices for AI systems.

The following are four recommendations of steps that could be taken to strengthen America’s ability to manage the impact of AI and related technologies within the context of U.S.-China relations:

Mutual reassurance. The level of senior-level official communication on AI and related technologies lags significantly behind the potential impacts these could have on the bilateral relationship. Leaders in both countries could jointly reaffirm that their objective is (1) to address forthrightly and manage constructively areas where the introduction of AI and related technologies will elevate competition and (2) to encourage collaboration in areas where both sides would benefit from greater coordination and cooperation. Such parallel messaging would create a demand signal for leading thinkers on both sides of the Pacific to prioritize these topics in upcoming exchanges.

Maintain perspective. Chinese AI researchers and developers are not ten-foot-tall giants. China’s plan to overtake the United States and dominate the future of AI innovation is more ambition than achievement to date. To be sure, Chinese researchers and developers enjoy significant relative advantages, including extensive government support, data abundance, and a hyper-competitive entrepreneurial environment, all of which accelerates the pace of innovation.11 But China’s path to unalloyed dominance is far from assured. China also confronts some serious challenges, including an uncertain data regime and lack of global players in key data sectors; government pressure on technology companies to ensure some advances serve the needs of the Communist Party; a more restrictive global environment for acquiring cutting-edge technologies abroad; the possibility that the government could, in the future, pressure Chinese firms to procure components domestically rather than from globally integrated supply chains; the risk of government-directed investment leading to speculative boom and bust cycles; and the potential for AI to become socially divisive as it is employed domestically for intrusive surveillance or targeted repression. Additionally, while much is made of China’s data abundance, it is worth bearing in mind that Google and Facebook each have more users globally on their platforms than the entire population of China, with further growth potential still. America’s confidence in its ability to compete with China matters greatly. If the United States feels back-footed by the pace of China’s technological advances, it naturally will be less comfortable exchanging lessons learned with China on shared AI-related challenges.

Invest in strengths. The United States holds three core advantages when it comes to innovation – education, immigration, and investment. With its ability to draw the best minds from around the world, its world-class university system, and its deep and efficient capital pools, the United States should welcome healthy and fair competition with China. The United States needs to advance a proactive strategy to build on these strengths – for example, by easing the immigration process for leading innovators; resurrecting efforts to strengthen the research triangle between academia, government labs, and the private sector; and maintaining policies that attract capital to American shores.

Find our friends. Through coordination with allies and partners, the United States will be better able to harmonize national export controls, defense trade controls, and investment review mechanisms to limit transfer of dual-use technologies to China. The United States will have greater impact in pressing China to curb actions it finds unacceptable if allies and partners join the chorus. The United States also would benefit from increasing collaboration with allies to accelerate AI-enabled advances in defense innovation.

Conclusion

In the AI age, no other country likely will catch up to the United States or China in terms of technological development or national power, and the United States and China each will not be able to dominate or impose its will on the other, at least in peacetime conditions. Both stand to gain if they find ways to learn from each other’s experiences navigating the information frontier, or to lose if they descend into unvarnished confrontation or conflict.

Instead of viewing AI in zero-sum, Cold War–like terms, the United States and China need to make deliberate efforts to adopt a more balanced narrative.

In order for both sides to manage tensions and maintain open channels for cooperating in areas of overlapping interest, the paradigm of AI’s role in the bilateral relationship will need to change. Instead of viewing AI in zero-sum, Cold War–like terms, the United States and China need to make deliberate efforts to adopt a more balanced narrative. At a time of intensifying rivalry, this shift will not come easily or naturally, but the costs and consequences of the alternative should awaken both sides to the value in doing so.

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By Ryan Hass, Zach Balin
Under President Donald Trump, great power competition has become the organizing principle of American foreign policy. This has led to near-daily invocations of the Cold War to describe the intensifying rivalry between the United States and China, and to frequent analogies to an “arms race” to describe bilateral competition in advanced technologies, including quantum computing and artificial intelligence (AI). Public statements and national plans from both governments have reinforced this zero-sum dynamic. Such framing has done more to conceal than clarify and, if taken to its logical end-point, will do more harm than good for the United States.
AI will create both immense stress on the U.S.-China relationship as well as opportunities for potential collaboration.
This paper argues that we need a different narrative to describe the role of AI in the escalating competition between the United States and China. Even as artificial intelligence is contributing to an intensifying bilateral rivalry, it also is driving both countries to race out ahead of the rest of the world in innovation, economic growth, and overall national power. Moreover, the adoption of advanced technologies is hastening the arrival of intense societal disruptions in both countries. AI applications are also exacerbating ethical questions about government’s role in protecting individual liberties, and elevating the competition between authoritarian capitalism and liberal democracy. To focus on only one of these dynamics would be to lose sight of the bigger picture: AI will create both immense stress on the U.S.-China relationship as well as opportunities for potential collaboration. The core challenge for U.S. policymakers will be to manage the stresses induced by AI in a way that preserves political space for working together when it serves American interests to do so. Along with other essays in our AI policy series, this piece offers recommendations on how best to do so.
How did we get here?
In March 2016, a Google system powered by an AI algorithm squared off against Lee Sedol, an 18-time world champion in the famously complex game of Go. In front of an audience of more than 280 million mostly Chinese viewers, the Google system triumphed, plunging China into what renowned technologist Kai-Fu Lee described as an “artificial intelligence fever” that “lit a fire under the Chinese technology community that has been burning ever since.”1
A little over one year later, in July 2017, China unveiled its national plan for seizing the spoils of AI. The “New Generation AI Development Plan” set targets and pledged national resources, calling for China to catch up on AI technology and applications by 2020, achieve major breakthroughs by 2025, and become a global leader in AI by 2030. President Xi Jinping reinforced these themes in his 19th Party Congress speech in October 2017 and in a major Politburo study session in late October.
Further stoking unease has been some of China’s official rhetoric, which promotes military-civil fusion of technological development to degrade America’s competitive edge. Such unease has been amplified by China’s ambitious Belt and Road Initiative, a massive global initiative that some in the United States fear will enable Beijing to set global technological standards. Cumulatively, China’s efforts have fed what Dean Garfield, president and CEO of the Information Technology Industry Council, has characterized as a newfound “hysteria” in Washington that America is losing its innovation edge to China.2
Americans are unaccustomed to other countries publicly projecting plans to displace them. As former Secretary of State Condoleezza Rice recently said, “When we see [China]…say, ‘We’re going to do whatever it takes to surpass the United States’… you’re going to get ... By Ryan Hass, Zach Balin
Under President Donald Trump, great power competition has become the organizing principle of American foreign policy. This has led to near-daily invocations of the Cold War to describe the intensifying rivalry between the ... https://www.brookings.edu/research/how-artificial-intelligence-will-affect-the-future-of-energy-and-climate/How artificial intelligence will affect the future of energy and climatehttp://webfeeds.brookings.edu/~/591426888/0/brookingsrss/topics/technology~How-artificial-intelligence-will-affect-the-future-of-energy-and-climate/
Mon, 30 Nov -0001 00:00:00 +0000https://www.brookings.edu/?post_type=research&p=556683

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By David G. Victor

In a 2017 article for Foreign Affairs, Kassia Yanosek and I advanced the hypothesis that the biggest impacts of the information technology (IT) revolution may be felt far outside IT—in the traditional industries of oil, gas, and electricity.1 That’s because IT was transforming how those industries function. That logic of transformation may be especially profound when looking at a subset of the IT revolution: artificial intelligence (AI).

Other essays in this series explain what’s happening with AI and why it is such an important technical revolution.2 In this essay, I’ll look at how AI might be affecting the supply and demand for energy and the implications of AI for how modern society uses energy: climate change. In a nutshell, the message is that AI helps make markets more efficient and easier for analysts and market participants to understand highly complex phenomena—from the behavior of electrical power grids to climate change.

But AI itself won’t assure that outcome without clear policy incentives. Ironically, extremely smart energy markets lubricated by AI may make it easier to design good policy incentives while also making it easier for consumers to make choices about which energy services and products to buy that avoid the need to cut emissions. Even a big effort to control emissions will leave a lot of climate change—meaning that, in the future, much of “climate policy” will be focused on adapting to climate impacts and implementing quick responses in case of climate emergencies.3 Extremely intelligent systems for adapting to climate change impacts may make the cost of that adaptation more transparent and thus politically difficult to muster.

AI helps make markets more efficient and easier for analysts and market participants to understand highly complex phenomena—from the behavior of electrical power grids to climate change.

The impacts of AI are numerous, but four clusters of impacts seem most likely to affect energy and climate—two will alter the supply and demand for energy, and two will affect the ability of societies to understand how emissions are affecting the climate and how to manage those impacts.

AI impacts on energy supply

Most visible in the energy and climate space is the impact of AI on how energy is supplied. That’s because more intelligent energy supply systems, in effect, shift outward the supply curves. They take resources that are hard to tap and lower the cost. For example, machine learning systems can improve the ability to map and understand the size and value of underground deposits of oil and gas—in turn, making it easier to tap those resources at lower cost.

The same logic applies not just to traditional hydrocarbons that make up the backbone of the world’s energy system but also new non-hydrocarbon energy supply options. For example, AI-assisted training for the design and operation of wind and solar farms can make these systems much more efficient in how they take financial resources (i.e., capital) and generate electricity. In the case of wind farms, the turbine heads can be oriented actively to capture a greater fraction of the incoming wind—something that has been doable for a long time and can be made more efficient with machine learning. Similar learning can improve the quality of solar forecasting—for example, leading to better day-ahead and hour-ahead predictions of how clouds and other weather formations affect solar output. In turn, better forecasts can make it easier and more lucrative for solar generators to participate in electricity markets.

An interesting question is whether there is a “bias” in how AI-related technologies are affecting energy supply, such as whether they making traditional hydrocarbon suppliers more productive faster than they make zero-carbon renewables more productive? This is a hard question to answer because it requires disentangling the effects of many other technological changes (e.g., improved drill bits, control systems for horizontal drilling, better materials for wind turbine blades, and less costly solar cells) from the specific effects of AI. At the moment, my sense is that AI is having a bigger impact in oil and gas than in renewables because the kinds of activities that are unlocking new hydrocarbon resources—notably the shale revolution in oil and gas which requires mapping complex underground reservoirs and tailoring drilling methods4—are particularly well-suited to the recursive, complex learning processes that AI is well-suited to deliver.

AI impacts on energy demand and markets

While it isn’t clear whether AI will favor higher or lower carbon supplies of energy, the impacts of AI on energy demand are easier to pin down. All else being equal, systems that have large amounts of intelligence—and the capacity to update quickly in light of real-world conditions—are probably systems that are a lot more efficient. Efficiency will lower demand for energy and lower emissions. The effects are likely larger than one percent—already, simple “nudge” interventions in power markets, for example reminding customers about the need to reduce energy consumption during peak periods and changing the default settings on thermostats—yield energy savings up to a few percentage points. A hyper-smart AI-driven energy system should deliver even bigger reductions, in part because the changes needed (e.g., aligning energy consumption with real-time changes in energy markets) can be automated. That said, the savings are unlikely to be as large as the 60 to 100 percent reduction in emissions that scientists say will be needed in order to stop global warming.

While it isn’t clear whether AI will favor higher or lower carbon supplies of energy, the impacts of AI on energy demand are easier to pin down.

One of the great promises of adding AI to energy markets lies with linking what customers want (e.g., light and heat) with the exact range of options and market conditions for supplying those energy services. Machine learning is ideally suited for making fine-grained determinations of what customers want and then adjusting energy purchasing decisions accordingly. In theory, they could make a number of services that are already offered in today’s markets more powerful, such as:

Purchasing green energy credits. Today, customers typically either “go green” or don’t. What they pay for green is a decision made rarely (often just at the time when a customer signs up). AI systems could better embed information about what customers are willing to pay for green energy and also offer different shades of green. Today, the green debate focuses on renewable power almost exclusively. In the future it could include other offers—for example, output from new nuclear reactors that are emission free.

Adjusting power purchasing decisions. As power grids shift to play a much larger role for variable renewable generators, the price of power will become more variable, creating a greater social value from real-time adjustments in power purchasing. In California, for example, power prices may reliably become negative mid-day (when solar output is highest) and then spike as the sun sets but demand for air conditioning and lighting rises in the late afternoon. This is one reason why the default tariff for electricity starting in 2019 will be “time of use.” AI can allow even small consumers to automatically adjust their power consumption in real time with prevailing prices—something that ordinary people won’t do unless they like sitting at home staring at real-time data from power markets. (Some people do that; most prefer to live their lives.)

Making electric supplies more reliable and bespoke to consumer needs. AI schemes could integrate data from hazards (e.g., extreme storms and fires) and then adjust grid operations accordingly—making the grid safer, more efficient, and more reliable. Already, a few utilities have installed self-healing grid systems—that is, automated surveillance and switching equipment that can identify faults on a grid, isolate them and restore power automatically. Conventionally, when the utility detects a fault—often because an irate customer calls, complaining of lost power—the response is to send a bucket truck with two guys who drive the lines, finding the trouble and then manually closing switches once the problem is fixed. Similarly, many customers now demand levels of reliability higher than what the grid can offer—and they purchase costly power conditioning, generation, and storage technologies. AI can help make purchase and operation of those systems much more efficient.

At present, the potential for these AI uses is barely tapped. A few utilities are experimenting with systems, some large customers are actively managing energy systems with AI-based systems (because they can afford to amortize the cost over large savings), and some firms like Stem are emerging as intermediaries—making explicit AI offers to customers and providing the expertise needed so that even small customers can utilize these systems.

How AI will improve climate modeling

Most human-caused changes in climate are rooted in how we use energy—in particular fossil fuels that, when combusted, intrinsically generate carbon dioxide (CO2). Thus, the changes discussed above—some leading to higher emissions, others to greater efficiency and lower carbon intensity—will affect the rate at which emissions flow into the atmosphere and accumulate. If the central message from the above discussion is that AI makes it possible for energy markets to reflect real-world conditions—and to be more efficient in matching consumer preferences with supplies—then there is no reason to believe that these more efficient markets, on their own, will tackle the carbon problem. Instead, they will require overt policy signals. For years, it has been thought that people often don’t respond readily to price signals, which is one reason why many analysts (and an even larger fraction of politicians) like direct regulation as a means of inducing reductions in emissions. Better and more efficient markets that can help consumers become more responsive to real-world conditions could help tamp down that enthusiasm for regulation and make practical a greater reliance on market-based instruments—such as carbon taxes.5

There is no reason to believe that these more efficient markets, on their own, will tackle the carbon problem. Instead, they will require overt policy signals.

AI could help radically improve the assessment of climate change. Today’s climate impact assessments rely on global-scale models of the climate system that are then downscaled to regional and local assessments. The downscaling process is complex and imperfect, in part because lots of local factors affect how broad changes in the climate are manifest where people actually live—along coastlines, near wildfire zones, in cities struggling with heat stress, and the like. AI makes it possible to connect the imperfect downscaling process with real information about actual impacts—reflected in insurance claims, weather extremes, the arrival of migrants, observed outbreaks, the spread of disease, and such. Already, the community of scientists that perform climate-impact studies are making use of such diverse data sources.6 AI could help automate and enrich that process, making real-time adjustments in climate-impact assessments feasible.

While these frontier opportunities are new, the idea of using machine feedbacks to improve model quality is hardly new in meteorology and climate modeling. For roughly two decades, there has been a systematic effort to compare climate models in terms of performance—that is, skill at forecasting temperature, precipitation, ice cover, and such. Those comparisons have probably made the models better and have also made the community much more aware of which models work best for different conditions. They have also made it easier for the Intergovernmental Panel on Climate Change (IPCC) and other bodies that conduct climate-science assessments to determine where the climate modeling community agrees and disagrees.

How AI will improve climate policy

Since the chief protagonist in the climate change story, CO2, has a long atmospheric lifetime, there is only a sluggish relationship between changes in emissions and the accumulated concentrations; in turn, those concentrations have a sluggish impact on the climate. Even if AI were part of some massive transformation in the energy system, the built-in inertia of that energy system, along with the inertia in the climate system, virtually guarantees that the world is in for a lot of climate change. All this is grim news and means that widely discussed goals, such as stopping warming at 1.5 or 2 degrees Celsius are unlikely to be realized.

These geophysical and infrastructural realities give rise to a new policy reality: adaptation is urgent.7 They also mean that emergency responses to extreme climate impacts—for example, solar geoengineering, might be needed as well.

Extreme climate change is going to be ugly and will require hard choices—such as which coastlines to protect or abandon. Without smart adaptation strategies, it will be a lot worse.

Existing research shows that there is a huge difference in the impact on public welfare from scenarios where climate change affects a society that doesn’t have an adaptation plan compared with a society that takes active adaptive measures. For example, the most recent U.S. climate-impact assessment released in November 2018 demonstrates that active adaptation measures can radically reduce losses from some climate impacts—often with benefits that far exceed the costs.8 Extreme climate change is going to be ugly and will require hard choices—such as which coastlines to protect or abandon. Without smart adaptation strategies, it will be a lot worse.

One of the central insights from the science of climate impacts is that extreme events will cause most of the damage. A world that is a bit warmer and wetter (and a bit drier in some places) is a world that societies, within reason, can probably adapt to—especially if those gradual changes are easy to anticipate. But a world that has more extreme events—put differently, climate events that have a higher variance—is a world that requires a lot more preparedness. A farming area that faces a new, significant risk of truly extreme drought for example, such as a decade-long dust bowl, will need to prepare as if that extreme event is commonplace. It will need irrigation systems, the option of planting hardier crops and other possible interventions that sit ready when the extreme events come.

Once those systems are purchased, much of the expense is borne and it makes sense to use them all the time. This has been the experience, for example, with the Thames river barrier or a similar Dutch flood barrier—these systems were designed and installed at vast expense with extreme events in mind, and now they are being used much more frequently. Climate impacts are, fundamentally, stochastic events centered around shifting medians—a warmer world, for example, is one where median temperature rises and where the whole distribution of temperatures from cold to hot shifts hotter. But the tails in that statistical distribution also probably fatten, and for some impacts, those tails get a lot fatter. Machine learning techniques will probably improve the ability to understand the shapes of those tails.

This logic of extreme events as the main drivers of climate impacts and response strategies has some big implications for how societies will plan for adaptation and how AI can help—possibly in transformative ways.

First, AI can help focus and adjust adaptation strategies. Because uncertainty is high and extreme events are paramount, policymakers, firms, and households will not know where to act nor what expense is merited. They will have a large portfolio of responses, each with an option value. Machine learning can help improve the capacity to assess those option values more rapidly. Such techniques might also make it possible to rely more heavily on market forces to weigh which options generate private and public welfare—if so, AI could help reduce one of the greatest dangers as societies develop adaptation strategies, which is that they commit vast resources to adaptation without guiding resources to their greatest value. High levels of uncertainty, along with acute private incentives that can mis-allocate resources—for example, local construction firms and organized labor might favor some kinds of adaptive responses (e.g., building sea walls and other hardened infrastructure) even when other less costly options are available—mean that adaptation needs could generate a massive call on resources and thus a massive opportunity for mischief and mis-allocation.

Second, most adaptation efforts are intrinsically local and regional affairs. As a matter of geophysics, climate change harms public welfare when general perturbations in the oceans and atmosphere get translated into specific climatological events that are manifest in specific places—specific coastlines, mountainous regions, public lands, and natural ecosystems. As a matter of public policy, the actors whose responses have the biggest leverage on local impacts are managers of local infrastructures—coastal and urban planners, developers, city managers, and the like. Politically, this is one of the reasons why, despite all the difficulties in mobilizing action to control emissions, it is likely that as communities realize what’s at stake with adaptation, they will respond. Local responses generate, for the most part, local benefits. A big challenge in all this local response, however, is that local authorities are intrinsically decentralized and usually not steeped in technical expertise. Getting the best information on climate impacts and response strategies—let alone keeping that information aligned with local circumstances and shifting odds for climate impacts—is all but impossible. AI could help lower that cost and, in effect, democratize quality climate impacts response.

Third, and perhaps most importantly, the single strongest result from studies assessing climate impacts is that poor communities will be hit harder than the rich. That’s because responding to climate impacts is often costly and because the kinds of factors that help explain wealth—good government and low levels of corruption—also explain which societies are most likely to adopt smart adaptation measures. The poor are on the front lines of climate change and, for the most part, will suffer the most as reflected in relative loss of welfare and loss of life and opportunity. To the extent that AI democratizes and improves the quality of understanding and responding to climate impacts, it may differentially help the societies that otherwise would be least able to respond. From a climate perspective, then, the AI revolution could end up being a vitally important part of assuring and promoting economic development amongst the world’s least well off—a topic that another paper in this series explores.9

Final thoughts

To close, I will make two points. First, AI has the potential to make energy markets a lot more powerful—by unlocking new supplies, reducing transaction costs, and making it easier for users to specify what they want to buy in the market. Efficient markets are great, but they also mean that market failures can become more rampant. Climate change is perhaps the largest market failure the world has seen so far—emissions of warming gases have global external consequences and the failure to impose emission taxes or other incentives means that firms and individuals are causing higher emissions and greater externalities than warranted. AI can transform markets, but the markets won’t transform emissions without clear signals. Mustering the political ability to put those signals into place and coordinate across countries remains as much a problem today as it was 30 years ago—and AI has had essentially no impact on that.

Second, as the world grapples with climate impacts and adaptation, it will probably discover that the line between “adaptation policy” and “good development” gets blurry. Some of the most important adaptation policies are also among the most important development policies. For example, one way to cut the impacts of climate change on agriculture is to adopt early warning crop forecasting systems—so that farmers can adjust seeds, cropping methods, and planting times (among other variables) to reduce harmful impacts from the vagaries of weather. Those same systems make sense even without a changing climate. AI could help make these kinds of development-oriented activities and policies more efficient economically, and politically, administratively easier to adopt, and more democratic in their orientation.10 Insofar as that happens, AI will further blur the line between adaptation and development.

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By David G. Victor
In a 2017 article for Foreign Affairs, Kassia Yanosek and I advanced the hypothesis that the biggest impacts of the information technology (IT) revolution may be felt far outside IT—in the traditional industries of oil, gas, and electricity.1 That’s because IT was transforming how those industries function. That logic of transformation may be especially profound when looking at a subset of the IT revolution: artificial intelligence (AI).
Other essays in this series explain what’s happening with AI and why it is such an important technical revolution.2 In this essay, I’ll look at how AI might be affecting the supply and demand for energy and the implications of AI for how modern society uses energy: climate change. In a nutshell, the message is that AI helps make markets more efficient and easier for analysts and market participants to understand highly complex phenomena—from the behavior of electrical power grids to climate change.
But AI itself won’t assure that outcome without clear policy incentives. Ironically, extremely smart energy markets lubricated by AI may make it easier to design good policy incentives while also making it easier for consumers to make choices about which energy services and products to buy that avoid the need to cut emissions. Even a big effort to control emissions will leave a lot of climate change—meaning that, in the future, much of “climate policy” will be focused on adapting to climate impacts and implementing quick responses in case of climate emergencies.3 Extremely intelligent systems for adapting to climate change impacts may make the cost of that adaptation more transparent and thus politically difficult to muster.
AI helps make markets more efficient and easier for analysts and market participants to understand highly complex phenomena—from the behavior of electrical power grids to climate change.
The impacts of AI are numerous, but four clusters of impacts seem most likely to affect energy and climate—two will alter the supply and demand for energy, and two will affect the ability of societies to understand how emissions are affecting the climate and how to manage those impacts.
AI impacts on energy supply
Most visible in the energy and climate space is the impact of AI on how energy is supplied. That’s because more intelligent energy supply systems, in effect, shift outward the supply curves. They take resources that are hard to tap and lower the cost. For example, machine learning systems can improve the ability to map and understand the size and value of underground deposits of oil and gas—in turn, making it easier to tap those resources at lower cost.
The same logic applies not just to traditional hydrocarbons that make up the backbone of the world’s energy system but also new non-hydrocarbon energy supply options. For example, AI-assisted training for the design and operation of wind and solar farms can make these systems much more efficient in how they take financial resources (i.e., capital) and generate electricity. In the case of wind farms, the turbine heads can be oriented actively to capture a greater fraction of the incoming wind—something that has been doable for a long time and can be made more efficient with machine learning. Similar learning can improve the quality of solar forecasting—for example, leading to better day-ahead and hour-ahead predictions of how clouds and other weather formations affect solar output. In turn, better forecasts can make it easier and more lucrative for solar generators to participate in electricity markets.
An interesting question is whether there is a “bias” in how AI-related technologies are affecting energy supply, such as whether they making traditional hydrocarbon suppliers more productive faster than they make zero-carbon renewables more productive? This is a hard question to ... By David G. Victor
In a 2017 article for Foreign Affairs, Kassia Yanosek and I advanced the hypothesis that the biggest impacts of the information technology (IT) revolution may be felt far outside IT—in the traditional industries of oil, ... https://www.brookings.edu/research/enabling-opportunities-5g-the-internet-of-things-and-communities-of-color/Enabling opportunities: 5G, the internet of things, and communities of colorhttp://webfeeds.brookings.edu/~/591310568/0/brookingsrss/topics/technology~Enabling-opportunities-G-the-internet-of-things-and-communities-of-color/
Wed, 09 Jan 2019 13:00:29 +0000https://www.brookings.edu/?post_type=research&p=556148

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By Nicol Turner-Lee

Executive summary

Fifth-generation (5G) mobile networks are expected to be the next big leap in mobile broadband. Peak download speeds as high as 20 gigabits-per-second will enable specialized tasks like remote precision medicine, connected cars, virtual and augmented reality, and a wide array of internet of things (IoT) applications.

Nationwide, resilient 5G networks will be needed to accommodate the growing demand for high-speed mobile broadband. While some researchers and analysts suggest that existing 4G Long-Term Evolution (LTE) technology is sufficient for the majority of IoT use cases, this paper argues that only high-speed, high-capacity, low-latency 5G broadband networks will meet the demands of increasing data-intensive applications. Moreover, 5G will support the massive numbers of devices that will simultaneously access the network, which will be far more than 4G LTE can handle. As 5G enables IoT applications, like health care, education, energy and transportation, it is imperative that they operate as anticipated, without fail, every time.

Further, 5G will be a determining factor in whether or not mobile-dependent users fully partake in the global digital economy, especially as smartphones, cell phones, and other wireless-enabled devices become the only gateway to the internet for certain populations. For communities of color that often lack reliable broadband access, 5G represents increased economic opportunity through improved access to social services, such as health care, education, transportation, energy, and employment. While lower-income African-Americans and Hispanics have similar levels of smartphone ownership as whites in the United States, they are more likely to depend on mobile services for online access, which is why 5G networks must be widely available, affordable, and able to support emerging technologies that address public interest concerns.

One area for optimized 5G use will be IoT that can offer tremendous benefits to communities of color whose members are often on the wrong side of the digital divide. This paper explores the relationship between 5G networks and IoT applications, especially as more of these functions become enabled through advanced mobile networks. In this paper, I argue that 5G networks must be nationwide, affordable, and resilient to ensure that these populations benefit from emerging technologies.

By providing both ubiquity and some level of digital equity for marginalized groups, robust 5G networks will ensure these populations are not left behind.

This paper concludes with three policy and programmatic proposals for both government and the private sector to collaborate in the deployment of 5G, while deepening their capacity and reach to communities in the most need of high-speed broadband access. By providing both ubiquity and some level of digital equity for marginalized groups, robust 5G networks will ensure these populations are not left behind.

Introduction

Fifth-generation (5G) mobile networks are expected to be the next big leap in mobile broadband. With expected peak download speeds as high as 20 gigabits-per-second, 5G users will be able to download a full-length movie in seconds and enable specialized tasks and functions, including remote precision medicine, connected cars, virtual and augmented reality experiences, as well as the internet of things (IoT).

More than 500 billion IoT devices, from sensors, to actuators, to medical devices, will be connected to the internet by 2030, according to research from Cisco.1 The data collected, aggregated, and analyzed by IoT devices will deliver insights across a wide variety of platforms and services, from health care to artificial intelligence innovations. 5G networks will be needed to meet the requirements of these data-intensive IoT devices and related cloud services.

Nationwide, resilient 5G networks will also be needed to accommodate the growing demand for high-speed mobile broadband. While some researchers and analysts suggest that existing 4G Long-Term Evolution (LTE) technology is sufficient for the majority of IoT use cases, this paper argues that only high-speed, high-capacity, low-latency 5G broadband networks will meet the demands of data-intensive applications. High-capacity and high-throughput operations will also be supported through 5G networks, making scaled IoT deployments even more cost effective. As 5G and IoT are broadly applied to life-saving devices and applications in the areas of health care, energy and transportation, it is imperative that they operate as anticipated, without fail, every time.

Further, access to 5G networks will be a determining factor in whether or not mobile-dependent users fully partake in the digital economy, especially as smartphones, cell phones, or other wireless-enabled devices have become their only gateway to the internet.

Further, access to 5G networks will be a determining factor in whether or not mobile-dependent users fully partake in the digital economy, especially as smartphones, cell phones, or other wireless-enabled devices have become their only gateway to the internet. Currently, 95 percent of Americans own a cell phone and 77 percent have smartphones, according to the Pew Research Center.2 Ownership cuts across demographic groups with African-Americans and Hispanics showing high levels of mobile device ownership. For low-income segments of these populations, wireless connectivity is most likely their only online access.

While IoT and related applications are just one of many use cases powered by next-generation mobile networks, I argue that they offer the most promise for eliminating the disadvantages resulting from the digital divide, especially for certain segments of African-Americans and Hispanics who are severely marginalized or socially isolated. Exploring the relationship between 5G and IoT by drawing upon existing use cases, this paper makes the case for why the United States needs nationwide 5G networks to leverage access to both services and opportunities for these populations.

First, I will explore how access to high-speed broadband can benefit communities of color. Next, the capabilities of 5G networks will be discussed, followed by an overview of the numerous IoT and 5G-enabled applications that, if applied, can greatly benefit online minority users. Finally, the paper will outline three policy and programmatic proposals where the government and private sector can work collaboratively to prioritize nationwide deployment of 5G networks, while broadening their capacity and reach to communities in the most need of high-speed broadband access. Data from a national online poll of 2,000 respondents that I conducted will also be shared in the paper to highlight consumer opinions around 5G deployment and use.3

Broadband access for communities of color

Twenty-four million Americans lack access to fixed, residential high-speed broadband services, according to 2018 data from the Federal Communications Commission (FCC).4 This includes 13 percent of African-Americans, 11 percent of Hispanics, 35 percent of those lacking a high school degree, 22 percent of rural residents, and 37.2 percent of households that speak limited English.5 In this accounting for differences in income, age, education and other factors, many racial and ethnic groups also continue to lag behind whites in residential broadband adoption.

Despite these disparities, mobile access has converged among many of these subgroups. Seventy-seven percent of whites, 75 percent of African-Americans, and 77 percent of Hispanics own a smartphone, according to the Pew Research Center.6 For many higher-income whites, access to the internet via a smartphone supplements a high-speed, in-home broadband connection, while lower-income populations, less-educated, and younger Americans tend to be more smartphone-dependent, relying on mobile broadband as their primary and oftentimes sole connection to the internet.7 Further, 35 percent of Hispanics and 24 percent of African-Americans have no other online connection except through their smartphones or other mobile devices, compared to 14 percent of whites.8 Thirty-one percent of individuals making less than $30,000 per year regularly rely on their mobile device for internet access.9 Finally, urban residents also tend to be more smartphone-dependent at 22 percent compared to 17 percent of rural and suburban residents.

Many of these smartphone-dependent populations overlap with those impacted by higher rates of unemployment, disparate educational attainment and limited economic mobility. For example, unemployed and under-employed African-Americans may face challenges in meeting current workforce demands due to limited digital skills, training, and access to online job openings. Despite advances in education since the 1970s, African-Americans experience higher rates of unemployment, potentially attributed to the lack of digital access in an information-rich economy (Figure 1).

These disadvantages are compounded by an inability to interact with medical providers, complete homework assignments, and engage government services. As a result, certain African-Americans, like other vulnerable populations, are locked out of opportunities that could enhance their social and economic mobility. Meanwhile, providers who are unable to maintain contact with these populations may find themselves incapable of regularly monitoring chronic diseases, connecting clients to job opportunities in real-time, or assisting students with homework and research assignments in the absence of a physical classroom or library access.

Thus, 5G networks can unleash opportunities across a number of different dimensions for vulnerable populations and, at the most basic level, offer a reliable wireless connection that can reduce the less than desirable impacts of social isolation and disadvantage, which affect certain consumers of color. The next section explores 5G’s capabilities.

5G and the capabilities of next-generation mobile broadband

Each generation of mobile technology has ushered in faster and more reliable cellular and mobile internet connections, enabling a new suite of functional innovations for users. First-generation (1G) cell phones enabled mobile voice communications, while second-generation mobile networks (2G) facilitated more efficient and secure calling services, along with widely adopted mobile messaging services, or short message service (SMS). High-definition video streaming on smartphones and other multimedia applications were made possible by 3G and 4G LTE networks.

These new communications functionalities created new markets and immense value for the U.S. economy. Between 2006 and 2016, the digital economy grew at an average rate of 5.6 percent, accounting for 6.5 percent of the current dollar GDP, according to the Bureau of Economic Analysis.10 4G LTE contributed to this growth by supporting new digital enterprises, including shared economy apps like Uber, Lyft, and others. Ride-sharing service Uber used 4G LTE to drive its platform, leveraging the GPS location and navigation capabilities of smartphone devices. In its early stage of business, the company gave 4G-enabled handsets to its drivers to ensure the reliability and functionality of navigation systems.11 Since then, the company’s mobile platforms have supported customer reviews, shared itineraries, among other services.

Social media platforms, including Facebook, have also experienced major growth with the availability of advanced mobile technologies. Facebook’s expansion to mobile in 2007 led to more profitable advertising revenue and increased online subscribership.12

Compared to 4G LTE, 5G will bring higher bandwidths, lower latency, and increased connectivity to mobile broadband. That is, 5G will allow more data to travel faster over wider coverage areas. 5G bandwidths are projected to be 10 times higher than 4G LTE, which will contribute to the faster transmission of data, images and videos. Lower latency will also enable high-speed virtual and augmented reality video without delays or glitches. Mobile connectivity will be strengthened through “small cell” infrastructure, which will densify 5G wireless signals and improve their movement through concrete buildings, and walls. Small-cell antennas, which can be the size of a pizza box, will also enhance wireless service supporting more devices on the same network at the same time.

IoT use cases and people of color

Not surprising, IoT can be optimized on next-generation mobile networks. By definition, IoT refers to physical things connected to each other using wireless communications services.13 As a global data infrastructure, IoT devices will generate massive amounts of data, which can be used to streamline and improve a wide variety of services and industries. 5G will be an important input for IoT, especially for devices and applications that require high reliability, strong security, widespread availability, and in some cases, ultra-low latency.

Because 5G’s technical features can simultaneously support massive numbers of devices, certain segments of African-American and Hispanic populations may be able to access services that are insufficiently available in certain urban and rural communities.

Because 5G’s technical features can simultaneously support massive numbers of devices, certain segments of African-American and Hispanic populations may be able to access services that are insufficiently available in certain urban and rural communities.

When applied to the verticals of health care, education, energy use, and transportation, IoT can reduce the cost of service delivery, make more accurate decisions around outputs (including costs), and empower consumers around individual and community concerns. Many of the advanced technologies will be promising for more isolated and mobile-dependent populations, potentially solving some of their challenges. The remainder of this section describes these IoT use cases more generally.

A. Health care

In the U.S., one-in-two American adults suffer from a chronic disease, while one-in-four American adults have multiple chronic diseases.14 Compared to whites, people of color are disproportionately affected by a range of chronic diseases, especially heart disease and diabetes. For example, between 2011 and 2014, African-Americans were more likely to be afflicted by diabetes than whites (18 percent compared to 9.6 percent).15 Forty percent of African-Americans are also more likely to have high blood pressure with very little management and control of its treatment.

The life expectancy at birth for African-Americans, 75 years, is four years lower than for whites.16 For African-Americans in particular, IoT has the potential to facilitate remote diagnosis, foster adherence to prescribed interventions and medications, and assist in the administration of medical services, including appointment scheduling, insurance management, and treatment plans. For example:

Home health sensing, a critical intervention for chronic disease patients, uses the microphones in smartphones to replicate spirometers, which measure air flow in and out of lungs for patients with chronic obstructive pulmonary disease (COPD). The data collected is used by doctors to monitor the disease’s progression in patients in real-time.

Novartis, Qualcomm, and Propeller Health are also tackling COPD by connecting an inhaler device to a digital platform via a sensor that passively records and transmits usage data for patients.

Proteus Digital Health has developed ingestible sensors that aid in treatment adherence. This sensor generates a signal after medicine is taken, which relays the data to a smartphone application and eventually to the medical provider.17

In these examples, having the ability to transmit results to health care providers means fewer trips to the hospital and improved health monitoring for patients. While data is not available on how African-Americans and Hispanics are specifically engaging these IoT applications, it is worth noting that each of these innovations are attempting to remedy the health care gaps caused by the physical or social isolation of patients. When matched with the historical data on certain chronic diseases affecting African-Americans and Hispanics, IoT health care applications can help address the disparate conditions that restrict access to primary and supportive patient care. Next-generation mobile networks can also spur the development of other emerging health care devices and applications.

B. Education

Historically, students of color have faced persistent educational disparities that unfortunately reflect differences in their socioeconomic status. While educational gaps have narrowed between whites and people of color on fourth and eighth grade math tests and fourth grade reading tests (benchmarks for student performance), African-Americans have lagged behind whites and Hispanics in educational attainment.18 Further, three-fourths of minority students attend schools where a majority of their classmates qualifies as poor or low-income compared to one-third of whites.19

IoT educational solutions can potentially contribute to more vibrant and robust school learning environments.

These statistics, coupled with the “homework gap,” or the barriers that students face when they don’t have broadband at home, further stifle educational attainment for disadvantaged populations. Data from my national survey shows that use of the internet for homework is lowest among Hispanic (2.4 percent) and African-American (2.5 percent) respondents, which could be attributed to an insufficient or non-existent broadband connection. Universal service programs, such as Lifeline and E-Rate, can help to alleviate some of the barriers to low-income broadband adoption, but they are not wholly sustainable by themselves to level the playing field for students of color.20

In line with the argument in this paper, IoT educational solutions can potentially contribute to more vibrant and robust school learning environments, including:

Interactive whiteboards;

eBooks;

Tablets and mobile devices;

3-D printers;

Student ID cards; and,

Student tracking systems.21

IoT can also personalize the learning experience for students by tailoring lessons to the student’s pace and style of learning, and capturing more data about the factors that boost their performance with every lesson.22 One such application is the result of IBM’s partnership with the textbook publisher Pearson to create software that allows students to ask questions, provides helpful feedback to the student, and keeps instructors updated on student progress.23 But, these applications and others require high-bandwidth connections, which are often not available or consistent in lower-income neighborhoods.

IoT technologies can also expand the possibilities for what and where students learn. Leveraging IoT, students of color can collaborate with each other and teachers in real time regardless of distance.24 For example, using virtual reality headsets, students in remote locations can place themselves in a classroom with their peers or transport teachers and students anywhere in the world (or universe) that the curriculum takes them, from inside the human body to the far reaches of the solar system.25 For students of color in less digitally connected schools, these technologies can make a marked difference in educational outcomes.

In addition to these classroom possibilities, some schools are also engaging IoT applications to:

Embed RFID chips in ID cards to track the presence of students, enabling tracking of tardiness and absenteeism and logging of students’ presence on campus.26

Deploy GPS-enabled bus systems where routes can be tracked so parents and administrators know where a given bus is at any time. Students can also be notified when the bus is near their pickup location to avoid long waits.

While these applications can operate over today’s 4G LTE networks, the affordability, scalability, and accessibility of 5G is projected to make these tools even more effective and precise.

C. Transportation

Another noteworthy utility is 5G’s capacity to support machine-to-machine communications. This is crucial for the deployment of safe, reliable, and efficient autonomous vehicles, which need vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications support. Intelligent vehicles have been shown to reduce traffic congestion, road accidents, and improve consumer mobility–all benefits of particular interest to African-American and Hispanic populations because of various factors:27

Hispanics and African-Americans experienced a higher rate of pedestrian deaths from 2005 to 2014 (1.40 and 1.74 per 100,000 people, respectively) than whites or Asian-Americans (both .93 deaths per 100,000 people).28 5G-enabled smart vehicles can significantly reduce such accidents owing to their enhanced sensors.

An observational study conducted by the University of Alabama-Birmingham showed that significant disparities in mobility exist between older African-Americans and whites, which propel disparities in functional ability and physical performance.29 For elderly people of color—in particular those who live in more rural and remote areas— autonomous vehicles can be a part of the process of aging-in-place by offering some level of independence.

People of color are more likely to be affected by high levels of air pollution due to residential location. Overall, nitrogen dioxide concentrations for nonwhites were 37 percent higher than for whites in 2010.30 Autonomous vehicles communicating over 5G networks with each other and with smart transportation infrastructure are projected to reduce traffic congestion.31 The less time that vehicles spend idling in traffic the fewer pollutants are emitted, leading to better health outcomes in communities where minorities live.

For elderly people of color—in particular those who live in more rural and remote areas— autonomous vehicles can be a part of the process of aging-in-place by offering some level of independence.

But, autonomous vehicles need wide area network infrastructure to operate.32 In the absence of 5G networks with the low-latency to support these transportation solutions, low-income customers in both urban and rural communities are more likely to become victims, rather than beneficiaries of these emerging transportation technologies, simply because their communities are unable to deploy reliable and resilient communications networks.

D. Energy

5G can support wider adoption of clean energy by enabling smart grids that integrate wind, solar, and other renewable sources into existing grids.33 Because wind and solar power are more decentralized and weather-dependent, electricity grids will need fast and reliable communications over 5G networks to switch power sources dynamically based on availability. Smart grids can expand access to renewable energy sources to all electricity customers without the price increases associated with customers exiting the grid, which disproportionately affects low-income communities of color.34

The gap in availability of clean energy between low-income communities of color and others will also have devastating consequences if IoT and 5G technologies are not equitably deployed. Generally, African-American and Hispanic households spend 7.2 percent of household income on utility services, or three times more than other households (2.3 percent).35 Thus, the deployment of 5G-enabled smart grids and smart household meters must anticipate and avoid potential income disparities in access to new energy technology.

5G’s direct impact on employment

African-Americans and Hispanics are also positioned to directly benefit from the workforce opportunities resulting from 5G deployment and use.

African-Americans and Hispanics are also positioned to directly benefit from the workforce opportunities resulting from 5G deployment and use. A recent report from Accenture estimates that the transition to 5G will create 50,000 new construction jobs in the U.S. to install new wireless infrastructure over a seven-year period.36 During a public event, FCC Commissioner Brendan Carr stated that small-cell deployment would create 27,000 jobs.37 These numbers do not include additional economic growth from expanding broadband access to Americans. The adoption of 5G technology into the broader economy could also create an additional 2.2 million jobs.38 Available 5G networks will also be able to connect job seekers to more diverse labor opportunities by enabling more telecommuting through videoconferencing and other remote applications. And, faster connection speeds can help individuals learn new skills through online courses and certifications. This will be critical in ensuring people of color are not further disadvantaged due to a lack of digital or other relevant skills.

In conclusion, high-speed, next-generation broadband networks and IoT, along with the technologies and applications they will enable, could greatly benefit people of color and position them for the emerging pathway to economic and social opportunities.

Policy recommendations

Looking ahead to 5G deployment, this next section outlines three policies, which should be priorities as the government and the private sector seek to realize the full value of advanced mobile services and ensure that certain segments of African-American and Hispanic populations are not left behind.

5G solutions must be able to bolster capacity, speed, and coverage to reach more populations of color.

Efforts to deploy 5G networks must focus on achieving ubiquitous service to minority populations that offers high capacity and speed. Several wireless carriers have already announced plans to launch 5G within certain U.S. cities.39 Whether the product is being pushed as a substitute for fixed broadband or a complementary mobility solution, emerging 5G networks are expected to offer services beyond traditional mobile services and video, which are two popular use cases for consumers.

While some industry leaders are experimenting with millimeter-wave or higher spectrum frequencies, these bands alone may not be sufficient to penetrate urban structures or go the distance in rural communities, where some of these lower-income consumers live. Because millimeter-wave spectrum transmits at frequencies between 24-79GHz, one of the shortcomings of these higher-frequencies is the reduced ability to travel through buildings, foliage, rain, or other obstacles, as well as go an adequate distance even in unimpeded spaces. Addressing these coverage challenges will be crucial in expanding national broadband access and allowing users to seamlessly take advantage of 5G, IoT, and other next-generation applications.

Given the limitations of millimeter-wave signals, there is a case for the greater use of low-band, or 600–700 MHz spectrum and cellular Specialized Mobile Radio, especially for improved in-building and more rural coverage. Models that embrace a multi-band spectrum approach that leverages both high-, mid-, and low-bands would best serve minority populations and their use of IoT applications and devices by providing greater coverage. This is particularly significant to low-income communities of color, who receive 15 percent less cell phone coverage than their wealthier counterparts, which can largely be due to where they live and their choice of wireless providers.40 By promoting efforts to ensure that wireless carriers have adequate access to combined mid- and low-band spectrum, policymakers can promote some level of broadband coverage in both urban and rural communities.

Policymakers can also encourage the expeditious deployment of small cells, which will also be critical in serving minority populations who are vastly concentrated in urban areas. Local governments should support the streamlining of siting and permitting processes and standardize pricing on pole attachments. Slow and expensive permitting could not only stifle 5G deployment in these communities, but also lead to slower network upgrades, resulting in lags in the functions of critical IoT applications in health care, public safety, and other areas. In the end, cities run the risk of foreclosing on the opportunities presented by 5G networks through delayed and stalled small-cell rollouts.

Slow and expensive permitting could not only stifle 5G deployment in these communities, but also lead to slower network upgrades, resulting in lags in the functions of critical IoT applications in health care, public safety, and other areas.

From an infrastructure perspective, combined spectrum opportunities that broaden both the capacity and coverage in all communities, along with the blanketing of small-cell antennae, are both reasonable measures that promote both ubiquity and some level of digital equity for marginalized populations and their communities.

5G must be affordable for consumers, despite massive telecom investments and costs.

5G investments are speculated to increase GDP by $500 billion.41 However, 5G networks will be expensive to deploy, particularly as wireless carriers are projected to invest in multiple network inputs, including spectrum, radio access network (RAN) infrastructure, transmission, and core networks. Telecom companies alone are expected to invest $275 billion over the next seven years in building out 5G networks.42 Some analysts have suggested that about $200 million will be spent in the 5G deployment in the first few years of service, while other analysts are projecting a $2.4 trillion spend between 2020 and 2030.43 The largest expenditure for many wireless carriers will be in small cells to drive wireless capacity.

These massive investments may prompt wireless carriers to either subsidize 5G investments, at least in the short-term, or consider passing these costs on to consumers, which could deter widespread adoption.

In the national online survey of 2,000 respondents that I conducted as part of this paper, 47 percent of respondents shared that they would not pay more to double or triple their current speeds. Given this finding, service providers will have to exercise more flexibility in pricing and data caps to ensure affordability and to drive consumer demand for faster networks.

Since 5G will allow for a multiplicity of functions, opportunities should exist for tiered or pre-paid pricing structures that can account for possible cost savings to consumers. For example, some of these savings could come from new market opportunities, including home video or cloud-based services, while other savings could result from 5G’s ability to operate in licensed and unlicensed spectrum, which could offer deeper and more flexible coverage that also results in reduced costs to consumers.

In addition, massive IoT and greater capacity to support scaled deployments of devices is expected to result in lower unit costs. Private sector solutions that leverage multiple spectrum bands, as previously discussed, could also reduce 5G costs by covering more areas and making services available to more low-income users—increasing the volume of subscribers.

There is also a chance that many mobile users will likely reach their monthly cap if data consumption trends escalate as projected. Given these possibilities, it will be important for wireless providers to offer a range of mobile service plans, including unlimited data options, bundles, or pre-paid programs, to ensure affordability for consumers. In the move from 3G to 4G/LTE, subscribers used more data, largely due to the growth of internet-based applications. A 2013 study from Mobidia found that the data usage of 100,000 Android LTE users in the U.S., South Korea and Japan was higher with 4G/LTE.44 That is, LTE users consumed far more data than those using 3G. According to the study, LTE smartphone users in Korea used on average 2.2GB of data per-month compared to just under 1GB on 3G smartphones—a difference of 132 percent, compared to a 36 percent increase in the U.S. (or, around 1.3 GB LTE data compared to 956MB on 3G).

Overall, the Mobidia study concluded that the greater availability of data would lead to increased usage.45 The availability of 5G is already anticipated to fuel mobile data traffic growth. By 2021, a 5G connection will generate 4.7 times more traffic than the average 4G connection, according to research conducted by Cisco.46

Generally, consumers of color have benefited from pre-paid plans over the years, suggesting similar results could occur if these options were extended to 5G customers. For many smartphone owners, the monthly cost of maintaining a device can be a financial hardship, with 23 percent of subscribers having to cancel or shut off their service for a period of time due to cost.47 In fact, 44 percent of smartphone owners who make under $30,000 per year have done so, and African-Americans and Hispanics are twice as likely as whites to have done the same.48 When it comes to mobile service, lower-income smartphone users tend to subscribe to relatively low-cost plans (including pre-paid) and often find themselves cancelling their service due as a result of affordability concerns.49

While the monthly cost of 5G mobile service is not yet determined for consumers, more pre- and post-paid plans, and not less, should be encouraged in the marketplace to guarantee ubiquity in use. Further, more flexibility in data plans and not just rigid caps may be a more viable solution for consumers where cost matters.

While government programs, such as Lifeline, can also alleviate the economic burden for consumers, the discounts must be applied to mobile services, especially as they become the primary conduit to the internet.50 Once fully deployed, 5G services should be eligible for government subsidies targeted to mobile access to ensure the participation of historically disadvantaged and vulnerable populations in the digital economy.

5G networks must serve the public interest.

Much of this paper is focused on advancing some of the public-good applications of 5G and IoT technologies, such as health care and education. In the race to launch 5G networks ahead of international competitors, including China and Korea, government and industry leaders must keep promoting innovation and growth by emphasizing that next-generation mobile networks will help improve, if not save, the lives of millions of Americans by cultivating better access to social and institutional services.

In the race to launch 5G networks ahead of international competitors, including China and Korea, government and industry leaders must keep promoting innovation and growth by emphasizing that next-generation mobile networks will help improve, if not save, the lives of millions of Americans.

The recent White House memorandum on spectrum policy appears to be in sync with the national efforts to deploy 5G networks.51 Requesting the coordination of federal agencies on spectrum availability and sharing opportunities, the administration is at least suggesting the removal of federal and regulatory red tape to expedite build-out. The memorandum further designates a spectrum task force drawn from federal agency stakeholders to increase the sharing of scarce spectrum resources among federal agencies and the private sector so that more spectrum is available for commercial 5G wireless networks. The White House’s strategy will also enhance spectrum management through flexible-use licenses that allow for temporary use of spectrum bands.

The FCC has also been working to address outdated regulatory processes and barriers within local bureaucracies that stifle the deployment of local cell sites and other communications infrastructure. Similar to the White House, the agency is working to develop the optimal national criteria for advancing next-generation, mobile networks.52

These governmental efforts are critical in freeing up the resources required to operate reliable, resilient and nationwide 5G networks. With this type of support, companies can focus on 5G solutions and applications that advance the public good, whether through making dents in health and wellness disparities or helping students gain access to more equitable learning environments and communities. In either case, the increased availability of spectrum will create the allowances for more strategic and purposeful IoT applications that can support communities of color and other vulnerable populations.

Conclusion

5G represents increased economic opportunity through improved access to social services, such as health care, education, transportation, energy, employment, and even public safety for communities of color—and, frankly, any other vulnerable group—that lacks access to a reliable broadband connection. This attribute is particularly important for African-Americans and Hispanics who have become increasingly reliant on mobile networks for broadband connectivity, while experiencing a degree of isolation from institutional and social services.

5G represents increased economic opportunity through improved access to social services for communities of color—and, frankly, any other vulnerable group—that lacks access to a reliable broadband connection.

5G access will not only provide an online gateway, but it will also expose certain populations to myriad benefits, including those enabled by IoT, which can ultimately improve the quality of their lives.

As efforts to advance the new technology become more prominent among legislators, communications providers, and even some citizen groups, U.S. policymakers must work diligently to identify and support 5G network deployment and adoption nationwide, especially in ways that bring exponential benefit to Americans in need. Without these actions, certain populations will remain relegated to the wrong side of the digital divide, failing to realize the power and potential of existing and emerging technologies.

The author would like to thank Jack Karsten and Madhu Kumar for the research support that they provided for this report.

The Brookings Institution is a nonprofit organization devoted to independent research and policy solutions. Its mission is to conduct high-quality, independent research and, based on that research, to provide innovative, practical recommendations for policymakers and the public. The conclusions and recommendations of any Brookings publication are solely those of its author(s), and do not reflect the views of the Institution, its management, or its other scholars.

Support for this publication was generously provided by T-Mobile. Brookings recognizes that the value it provides is in its absolute commitment to quality, independence, and impact. Activities supported by its donors reflect this commitment.

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By Nicol Turner-Lee
Executive summary
Fifth-generation (5G) mobile networks are expected to be the next big leap in mobile broadband. Peak download speeds as high as 20 gigabits-per-second will enable specialized tasks like remote precision medicine, connected cars, virtual and augmented reality, and a wide array of internet of things (IoT) applications.
Nationwide, resilient 5G networks will be needed to accommodate the growing demand for high-speed mobile broadband. While some researchers and analysts suggest that existing 4G Long-Term Evolution (LTE) technology is sufficient for the majority of IoT use cases, this paper argues that only high-speed, high-capacity, low-latency 5G broadband networks will meet the demands of increasing data-intensive applications. Moreover, 5G will support the massive numbers of devices that will simultaneously access the network, which will be far more than 4G LTE can handle. As 5G enables IoT applications, like health care, education, energy and transportation, it is imperative that they operate as anticipated, without fail, every time.
Further, 5G will be a determining factor in whether or not mobile-dependent users fully partake in the global digital economy, especially as smartphones, cell phones, and other wireless-enabled devices become the only gateway to the internet for certain populations. For communities of color that often lack reliable broadband access, 5G represents increased economic opportunity through improved access to social services, such as health care, education, transportation, energy, and employment. While lower-income African-Americans and Hispanics have similar levels of smartphone ownership as whites in the United States, they are more likely to depend on mobile services for online access, which is why 5G networks must be widely available, affordable, and able to support emerging technologies that address public interest concerns.
One area for optimized 5G use will be IoT that can offer tremendous benefits to communities of color whose members are often on the wrong side of the digital divide. This paper explores the relationship between 5G networks and IoT applications, especially as more of these functions become enabled through advanced mobile networks. In this paper, I argue that 5G networks must be nationwide, affordable, and resilient to ensure that these populations benefit from emerging technologies.
By providing both ubiquity and some level of digital equity for marginalized groups, robust 5G networks will ensure these populations are not left behind.
This paper concludes with three policy and programmatic proposals for both government and the private sector to collaborate in the deployment of 5G, while deepening their capacity and reach to communities in the most need of high-speed broadband access. By providing both ubiquity and some level of digital equity for marginalized groups, robust 5G networks will ensure these populations are not left behind.
Introduction
Fifth-generation (5G) mobile networks are expected to be the next big leap in mobile broadband. With expected peak download speeds as high as 20 gigabits-per-second, 5G users will be able to download a full-length movie in seconds and enable specialized tasks and functions, including remote precision medicine, connected cars, virtual and augmented reality experiences, as well as the internet of things (IoT).
More than 500 billion IoT devices, from sensors, to actuators, to medical devices, will be connected to the internet by 2030, according to research from Cisco.1 The data collected, aggregated, and analyzed by IoT devices will deliver insights across a wide variety of platforms and services, from health care to artificial intelligence innovations. 5G networks will be needed to meet the requirements of these data-intensive IoT devices and related cloud services.
Nationwide, resilient 5G networks will also be needed to accommodate the growing demand for high-speed mobile ... By Nicol Turner-Lee
Executive summary
Fifth-generation (5G) mobile networks are expected to be the next big leap in mobile broadband. Peak download speeds as high as 20 gigabits-per-second will enable specialized tasks like remote precision ... https://www.brookings.edu/events/enabling-opportunities-5g-the-internet-of-things-and-communities-of-color/Enabling opportunities: 5G, the internet of things, and communities of colorhttp://webfeeds.brookings.edu/~/591318398/0/brookingsrss/topics/technology~Enabling-opportunities-G-the-internet-of-things-and-communities-of-color/
Tue, 08 Jan 2019 16:45:08 +0000https://www.brookings.edu/?post_type=event&p=556277

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Fifth-generation (5G) mobile networks are expected to be the next big leap in mobile broadband. Peak download speeds as high as 20 gigabits-per-second will enable specialized tasks like remote precision medicine, connected cars, virtual and augmented reality, and a wide array of internet of things (IoT) applications. Further, 5G will be a determining factor in whether or not mobile-dependent users fully partake in the global digital economy, especially as smartphones, cell phones, and other wireless-enabled devices become the only gateway to the internet for certain populations. Communities of color, who are often on the wrong side of the digital divide, are poised to benefit from 5G technologies, especially in its enablement of IoT applications in health care, education, transportation, and energy.

On January 23, The Center for Technology Innovation at the Brookings Institution will host a discussion on the relationship between 5G networks and IoT applications, with an emphasis on how it stands to benefit communities of color. Panelists will discuss why smartphone access and use matters to communities of color and explore how access to affordable and nationwide next-generation mobile networks can broaden the opportunities available to vulnerable populations.

After the session, speakers will take audience questions.

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Washington, DCupcoming15482592001548263700America/New_YorkFifth-generation (5G) mobile networks are expected to be the next big leap in mobile broadband. Peak download speeds as high as 20 gigabits-per-second will enable specialized tasks like remote precision medicine, connected cars, virtual and augmented reality, and a wide array of internet of things (IoT) applications. Further, 5G will be a determining factor in whether or not mobile-dependent users fully partake in the global digital economy, especially as smartphones, cell phones, and other wireless-enabled devices become the only gateway to the internet for certain populations. Communities of color, who are often on the wrong side of the digital divide, are poised to benefit from 5G technologies, especially in its enablement of IoT applications in health care, education, transportation, and energy.
On January 23, The Center for Technology Innovation at the Brookings Institution will host a discussion on the relationship between 5G networks and IoT applications, with an emphasis on how it stands to benefit communities of color. Panelists will discuss why smartphone access and use matters to communities of color and explore how access to affordable and nationwide next-generation mobile networks can broaden the opportunities available to vulnerable populations.
After the session, speakers will take audience questions. Fifth-generation (5G) mobile networks are expected to be the next big leap in mobile broadband. Peak download speeds as high as 20 gigabits-per-second will enable specialized tasks like remote precision medicine, connected cars, virtual and ... https://www.brookings.edu/blog/techtank/2019/01/07/will-this-new-congress-be-the-one-to-pass-data-privacy-legislation/Will this new Congress be the one to pass data privacy legislation?http://webfeeds.brookings.edu/~/590914796/0/brookingsrss/topics/technology~Will-this-new-Congress-be-the-one-to-pass-data-privacy-legislation/
Mon, 07 Jan 2019 12:00:34 +0000https://www.brookings.edu/?p=555991

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By Cameron F. Kerry

A Congress that begins with a government shutdown carrying over and a raft of subpoenas to the executive branch issued by incoming House committee chairs promises to be at least as polarized and partisan as its predecessor. Even so, legislators want to legislate, and will seek some opportunities for bipartisan agreement. One area where this may happen is federal legislation to protect personal information privacy.

Congressional leaders in both parties have expressed an interest taking up privacy legislation and are doing serious work to that end. Republican Senator John Thune of South Dakota, who chaired the Senate Commerce Committee and now becomes the majority whip, led a pair of privacy hearings last fall which he opened by saying developing a privacy law “enjoys strong bipartisan support” and “the question is no longer whether we need a federal law to protect consumers’ privacy. The question is what shape it should take.” His view was echoed by committee members on both sides.

His successor as committee chair, Republican Roger Wicker of Mississippi, has expressed support for “a federal law on the books by the end of 2019.” His incoming House counterpart, Democrat Frank Pallone of New Jersey, endorsed “comprehensive legislation” earlier in the year and, shortly after the election in November, announced that proposals for privacy and security will be part of the Democratic agenda.

So far the Senate has done the most visible work. Wicker along with Republican Jerry Moran of Kansas and Democrats Richard Blumenthal of Connecticut and Brian Schatz of Hawaii, all chairs or ranking member of relevant Commerce Committee subcommittees who are working on legislation, sent a joint letter to Commerce Secretary Wilbur Ross urging the administration to collaborate with Congress on privacy because national standards require congressional action.

Two senators have released drafts of bills intended to provoke discussion. Oregon Democrat Ron Wyden got an early jump with a draft Consumer Data Protection Act that caught attention with high-level corporate disclosure requirements similar to those in the Sarbanes-Oxley law, carrying a risk of criminal charges. Senator Schatz (joined by 15 Democrats) released a draft Data Care Act that would establish duties of “care, loyalty, and confidentiality” for online providers that handle personal data.

Two stakeholders have contributed their own draft bills to the discussion. Intel Corporation put out a draft on an interactive website featuring comments from privacy experts, based on codifying fair information practice principles. A Center for Democracy and Technology draft—developed after several months of input from academics, privacy groups, and businesses—spells out limits on data collection and use.

I’ll have more comments on these various draft bills and the substance of the emerging debate in the coming weeks. But, having led the Obama administration’s drafting of legislation based on its Consumer Privacy Bill of a Rights, I have great respect for any effort to put privacy into law. While there is a lot of agreement on the essential principles, it is a challenge to articulate these in ways that are concrete without being too prescriptive or too narrow.

What is striking to me is how far the discussion has come over the past couple of years. I have written about how the existing paradigm of U.S. privacy laws has become a losing game because it relies on consumer choice that puts the burden on individuals to manage their privacy and data. Emerging bills and the various frameworks and comments reflect a clear move toward shifting the burden onto companies to handle data fairly. After I left the government, draft Obama administration legislation was diluted in an unsuccessful effort to broaden business support, lost civil society support in the process, and so fell flat when it was released publicly. But now the Business Roundtable, Chamber of Commerce, and many other business interests are supporting consumer rights like access, correction, and deletion—levels of regulation that would have been dealbreakers when I was trying to broker legislation.

This reflects a climate that has changed in response to data breaches and concerns about data collection, the new European Union data protection regulation, and the California initiative drive that culminated in California’s broad new privacy law. This change got a boost from the Cambridge Analytica stories and growing concern about social media that has put privacy onto the congressional agenda for 2019.

Will this be enough to bring about passage? Any legislation is difficult, and big legislation that cuts across many different and powerful interests often takes several Congresses. But, as a committed optimist, it’s my belief that there is a sweet spot where business interests and privacy advocates can converge. There is a brief window for this to happen, because once California’s new privacy law takes effect at the beginning of 2020 and the next federal election takes shape, agreement is likely to become more difficult.

Privacy and consumer legislation have fared well in divided government. The cornerstones of federal privacy law, the Fair Credit Reporting Act and the Privacy Act, were enacted in 1974 when Republicans held the White House and Democrats the Congress. Another wave of privacy laws—the Health Insurance Portability and Accountability Act of 1996 and Gramm-Leach-Bliley Act of 1999 on financial privacy—was passed under the Clinton administration with Republicans in control of Congress. Whether privacy legislation can follow this pattern in 2019 will test whether this Congress seeks to pass legislation—or to build political brands.

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By Cameron F. Kerry
A Congress that begins with a government shutdown carrying over and a raft of subpoenas to the executive branch issued by incoming House committee chairs promises to be at least as polarized and partisan as its predecessor. Even so, legislators want to legislate, and will seek some opportunities for bipartisan agreement. One area where this may happen is federal legislation to protect personal information privacy.
Congressional leaders in both parties have expressed an interest taking up privacy legislation and are doing serious work to that end. Republican Senator John Thune of South Dakota, who chaired the Senate Commerce Committee and now becomes the majority whip, led a pair of privacy hearings last fall which he opened by saying developing a privacy law “enjoys strong bipartisan support” and “the question is no longer whether we need a federal law to protect consumers’ privacy. The question is what shape it should take.” His view was echoed by committee members on both sides.
His successor as committee chair, Republican Roger Wicker of Mississippi, has expressed support for “a federal law on the books by the end of 2019.” His incoming House counterpart, Democrat Frank Pallone of New Jersey, endorsed “comprehensive legislation” earlier in the year and, shortly after the election in November, announced that proposals for privacy and security will be part of the Democratic agenda.
So far the Senate has done the most visible work. Wicker along with Republican Jerry Moran of Kansas and Democrats Richard Blumenthal of Connecticut and Brian Schatz of Hawaii, all chairs or ranking member of relevant Commerce Committee subcommittees who are working on legislation, sent a joint letter to Commerce Secretary Wilbur Ross urging the administration to collaborate with Congress on privacy because national standards require congressional action.
Two senators have released drafts of bills intended to provoke discussion. Oregon Democrat Ron Wyden got an early jump with a draft Consumer Data Protection Act that caught attention with high-level corporate disclosure requirements similar to those in the Sarbanes-Oxley law, carrying a risk of criminal charges. Senator Schatz (joined by 15 Democrats) released a draft Data Care Act that would establish duties of “care, loyalty, and confidentiality” for online providers that handle personal data.
A broad array of stakeholders have been framing positions in anticipation of this discussion. Over the past several months, Access Now, the Business Roundtable, BSA | The Software Alliance, the Electronic Privacy Information Center, Google, the Internet Association, the Information Technology Industry Council, and the U.S. Chamber of Commerce all issued principles or frameworks outlining what legislation should address. Many more submitted comments in the National Telecommunications and Information Administration inquiry on national privacy standards.
Two stakeholders have contributed their own draft bills to the discussion. Intel Corporation put out a draft on an interactive website featuring comments from privacy experts, based on codifying fair information practice principles. A Center for Democracy and Technology draft—developed after several months of input from academics, privacy groups, and businesses—spells out limits on data collection and use.
I’ll have more comments on these various draft bills and the substance of the emerging debate in the coming weeks. But, having led the Obama administration’s drafting of legislation based on its Consumer Privacy Bill of a Rights, I have great respect for any effort to put privacy into law. While there is a lot of agreement on the essential principles, it is a challenge to articulate these in ways that are concrete without being too prescriptive or too narrow.
What is striking to me is how far the discussion has come over ... By Cameron F. Kerry
A Congress that begins with a government shutdown carrying over and a raft of subpoenas to the executive branch issued by incoming House committee chairs promises to be at least as polarized and partisan as its predecessor.https://www.brookings.edu/blog/techtank/2019/01/03/artificial-intelligence-and-bias-four-key-challenges/Artificial intelligence and bias: Four key challengeshttp://webfeeds.brookings.edu/~/590402340/0/brookingsrss/topics/technology~Artificial-intelligence-and-bias-Four-key-challenges/
Thu, 03 Jan 2019 19:00:04 +0000https://www.brookings.edu/?p=555804

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By John Villasenor

It is not news that, for all its promised benefits, artificial intelligence has a bias problem. Concerns regarding racial or gender bias in AI have arisen in applications as varied as hiring, policing, judicial sentencing, and financial services. If this extraordinary technology is going to reach its full potential, addressing bias will need to be a top priority. With that in mind, here are four key challenges that AI developers, users, and policymakers can keep in mind as we work to create a healthy AI ecosystem.

1. Bias built into data

We live in a world awash in data. In theory, that should be a good thing for AI: After all, data give AI sustenance, including its ability to learn at rates far faster than humans. However, the data that AI systems use as input can have built-in biases, despite the best efforts of AI programmers.

Consider an algorithm used by judges in making sentencing decisions. It would obviously be improper to use race as one of the inputs to the algorithm. But what about a seemingly race-neutral input such as the number of prior arrests? Unfortunately, arrests are not race neutral: There is plenty of evidence indicating that African-Americans are disproportionally targeted in policing. As a result, arrest record statistics are heavily shaped by race. That correlation could propagate in sentencing recommendations made by an AI system that uses prior arrests as an input.

The indirect influence of bias is present in plenty of other types of data as well. For instance, evaluations of creditworthiness are determined by factors including employment history and prior access to credit—two areas in which race has a major impact. To take another example, imagine how AI might be used to help a large company set starting salaries for new hires. One of the inputs would certainly be salary history, but given the well-documented concerns regarding the role of sexism in corporate compensation structures, that could import gender bias into the calculations.

2. AI-induced bias

An additional challenge is that biases can be created within AI systems and then become amplified as the algorithms evolve.

By definition, AI algorithms are not static. Rather they learn and change over time. Initially, an algorithm might make decisions using only a relatively simple set of calculations based on a small number of data sources. As the system gains experience, it can broaden the amount and variety of data it uses as input, and subject those data to increasingly sophisticated processing. This means that an algorithm can end up being much more complex than when it was initially deployed. Notably, these changes are not due to human intervention to modify the code, but rather to automatic modifications made by the machine to its own behavior. In some cases, this evolution can introduce bias.

Take as an example software for making mortgage approval decisions that uses input data from two nearby neighborhoods—one middle-income, and the other lower-income. All else being equal, a randomly selected person from the middle-income neighborhood will likely have a higher income and therefore a higher borrowing capacity than a randomly selected person from the lower-income neighborhood.

Now consider what happens when this algorithm, which will grow in complexity with the passage of time, makes thousands of mortgage decisions over a period of years during which the real estate market is rising. Loan approvals will favor the residents of the middle-income neighborhood over those in the lower-income neighborhood. Those approvals, in turn, will widen the wealth disparity between the neighborhoods, since loan recipients will disproportionally benefit from rising home values, and therefore see their future borrowing power rise even more.

Analogous phenomena have long occurred in non-AI contexts. But with AI, things are far more opaque, as the algorithm can quickly evolve to the point where even an expert can have trouble understanding what it is actually doing. This would make it hard to know if it is engaging in an unlawful practice such as redlining.

The power of AI to invent algorithms far more complex than humans could create is one of its greatest assets—and, when it comes to identifying and addressing the sources and consequences of algorithmically generated bias, one of its greatest challenges.

3. Teaching AI human rules

From the standpoint of machines, humans have some complex rules about when it is okay to consider attributes that are often associated with bias. Take gender: We would rightly deem it offensive (and unlawful) for a company to adopt an AI-generated compensation plan with one pay scale for men and a different, lower pay scale for women.

But what about auto insurance? We consider it perfectly normal (and lawful) for insurance companies to treat men and women differently, with one set of rates for male drivers and a different set of rates for female drivers—a disparate treatment that is justified based on statistical differences in accident rates. So does that mean it would be acceptable for an algorithm to compute auto insurance rates based in part on statistical inferences tied to an attribute such as a driver’s religion? Obviously not. But to an AI algorithm designed to slice enormous amounts of data in every way possible, that prohibition might not be so obvious.

Another example is age. An algorithm might be forgiven for not being able to figure out on its own that it is perfectly acceptable to consider age in some contexts (e.g., life insurance, auto insurance) yet unlawful to do so in others (e.g., hiring, mortgage lending).

The foregoing examples could be at least partially mitigated by imposing upfront, application-specific constraints on the algorithm. But AI algorithms trained in part using data in one context can later be migrated to a different context with different rules about the types of attributes that can be considered. In the highly complex AI systems of the future, we may not even know when these migrations occur, making it difficult to know when algorithms may have crossed legal or ethical lines.

4. Evaluating cases of suspected AI bias

There is no question that bias is a significant problem in AI. However, just because algorithmic bias is suspected does not mean it will actually prove to be present in every case. There will often be more information on AI-driven outcomes—e.g., whether a loan application was approved or denied; whether a person applying for a job was hired or not—than on the underlying data and algorithmic processes that led to those outcomes. This can make it more difficult to distinguish apparent from actual bias, at least initially.

While an accusation of AI bias should always be taken seriously, the accusation itself should not be the end of the story. Investigations of AI bias will need to be structured in a way that maximizes the ability to perform an objective analysis, free from pressures to arrive at any preordained conclusions.

The upshot

While AI has the potential to bring enormous benefits, the challenges discussed above—including understanding when and in what form bias can impact the data and algorithms used in AI systems—will need attention. These challenges are not a reason to stop investing in AI or to burden AI creators with hastily-drafted, innovation-stifling new regulations. But they do mean that it will be important to put real effort into approaches that can minimize the probability that bias will be introduced into AI algorithms, either through externally-supplied data or from within. And, we’ll need to articulate frameworks for assessing whether AI bias is actually present in cases where it is suspected.

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By John Villasenor
It is not news that, for all its promised benefits, artificial intelligence has a bias problem. Concerns regarding racial or gender bias in AI have arisen in applications as varied as hiring, policing, judicial sentencing, and financial services. If this extraordinary technology is going to reach its full potential, addressing bias will need to be a top priority. With that in mind, here are four key challenges that AI developers, users, and policymakers can keep in mind as we work to create a healthy AI ecosystem.
1. Bias built into data
We live in a world awash in data. In theory, that should be a good thing for AI: After all, data give AI sustenance, including its ability to learn at rates far faster than humans. However, the data that AI systems use as input can have built-in biases, despite the best efforts of AI programmers.
Consider an algorithm used by judges in making sentencing decisions. It would obviously be improper to use race as one of the inputs to the algorithm. But what about a seemingly race-neutral input such as the number of prior arrests? Unfortunately, arrests are not race neutral: There is plenty of evidence indicating that African-Americans are disproportionally targeted in policing. As a result, arrest record statistics are heavily shaped by race. That correlation could propagate in sentencing recommendations made by an AI system that uses prior arrests as an input.
The indirect influence of bias is present in plenty of other types of data as well. For instance, evaluations of creditworthiness are determined by factors including employment history and prior access to credit—two areas in which race has a major impact. To take another example, imagine how AI might be used to help a large company set starting salaries for new hires. One of the inputs would certainly be salary history, but given the well-documented concerns regarding the role of sexism in corporate compensation structures, that could import gender bias into the calculations.
2. AI-induced bias
An additional challenge is that biases can be created within AI systems and then become amplified as the algorithms evolve.
By definition, AI algorithms are not static. Rather they learn and change over time. Initially, an algorithm might make decisions using only a relatively simple set of calculations based on a small number of data sources. As the system gains experience, it can broaden the amount and variety of data it uses as input, and subject those data to increasingly sophisticated processing. This means that an algorithm can end up being much more complex than when it was initially deployed. Notably, these changes are not due to human intervention to modify the code, but rather to automatic modifications made by the machine to its own behavior. In some cases, this evolution can introduce bias.
Take as an example software for making mortgage approval decisions that uses input data from two nearby neighborhoods—one middle-income, and the other lower-income. All else being equal, a randomly selected person from the middle-income neighborhood will likely have a higher income and therefore a higher borrowing capacity than a randomly selected person from the lower-income neighborhood.
Now consider what happens when this algorithm, which will grow in complexity with the passage of time, makes thousands of mortgage decisions over a period of years during which the real estate market is rising. Loan approvals will favor the residents of the middle-income neighborhood over those in the lower-income neighborhood. Those approvals, in turn, will widen the wealth disparity between the neighborhoods, since loan recipients will disproportionally benefit from rising home values, and therefore see their future borrowing power rise even more.
Analogous phenomena have long occurred in non-AI contexts. But with AI, things are far more opaque, as the algorithm can quickly evolve to the point where ... By John Villasenor
It is not news that, for all its promised benefits, artificial intelligence has a bias problem. Concerns regarding racial or gender bias in AI have arisen in applications as varied as hiring, policing, judicial sentencing, and ... https://www.brookings.edu/events/how-china-and-the-u-s-are-advancing-artificial-intelligence/CANCELLED – How China and the U.S. are advancing artificial intelligencehttp://webfeeds.brookings.edu/~/590398488/0/brookingsrss/topics/technology~CANCELLED-How-China-and-the-US-are-advancing-artificial-intelligence/
Wed, 02 Jan 2019 16:59:49 +0000https://www.brookings.edu/?post_type=event&p=555538

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Brookings will be closed on Monday, January 14 due to inclement weather.
All events will be rescheduled for a future date.

Artificial intelligence is emerging as one of the most transformative technologies of our time. It powers autonomous vehicles, enables algorithms to operate, and is being applied in areas from health care and retail to finance and national defense. As AI begins to reshape entire industries and economies, the United States and China have emerged as pioneers at the leading frontier of this technological revolution. These two nations alone are expected to capture the bulk of the $15.7 trillion windfall that AI is projected to add to the global economy by 2030.

On January 14, The Center for Technology Innovation at the Brookings Institution will host a discussion on the potential impact of AI on U.S.-China relations, with an eye toward new developments, opportunities, and risks. Panelists will examine where each country stands in its AI developments, how the two nations are cooperating and competing, and what lies ahead.

After the session, speakers will take audience questions. This event will be webcast live.

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Washington, DCpast15474780001547481600America/New_York* Brookings will be closed on Monday, January 14 due to inclement weather.
All events will be rescheduled for a future date.
Artificial intelligence is emerging as one of the most transformative technologies of our time. It powers autonomous vehicles, enables algorithms to operate, and is being applied in areas from health care and retail to finance and national defense. As AI begins to reshape entire industries and economies, the United States and China have emerged as pioneers at the leading frontier of this technological revolution. These two nations alone are expected to capture the bulk of the $15.7 trillion windfall that AI is projected to add to the global economy by 2030.
On January 14, The Center for Technology Innovation at the Brookings Institution will host a discussion on the potential impact of AI on U.S.-China relations, with an eye toward new developments, opportunities, and risks. Panelists will examine where each country stands in its AI developments, how the two nations are cooperating and competing, and what lies ahead.
After the session, speakers will take audience questions. This event will be webcast live. * Brookings will be closed on Monday, January 14 due to inclement weather.
All events will be rescheduled for a future date.
Artificial intelligence is emerging as one of the most transformative technologies of our time.